Engineering in the Digital Age: A Career-Level Competency Framework Validated by the Productive Sector

Abstract

This study investigates the essential competencies for engineers in the context of digital transformation, with the aim of proposing a refined framework to guide professional development across career levels. A mixed-methods, sequential approach was adopted: (1) a systematic literature review, conducted between 2014 and 2024, which identified 46 competencies organized into seven dimensions; (2) a quantitative survey with 392 engineers who self-assessed their level of mastery for each competency; (3) semi-structured interviews with 20 company representatives, who validated and contextualized the essential competencies according to hierarchical levels (junior, mid-level, and senior); (4) data triangulation, resulting in a final competency model by career level. The findings reveal a widespread deficit in digital competencies, regardless of hierarchical level. In total, 33 competencies assessed by career level showed statistically significant differences in employer perceptions and were identified as progressive throughout the career trajectory. Analysis of self-assessments and interviews indicates that for early-career engineers, there is a strong emphasis on personal and basic cognitive competencies. For mid-level engineers, the data show a significant valuation of social competencies. Senior engineers are perceived as having accumulated experience across all seven mapped dimensions. This study offers a practical model that can be used by educational institutions, companies, and professionals to align education, market demands, and career planning.

1. Introduction

Amid the rapid technological advancements driven by digital transformation, human interaction and labor market demands have undergone substantial changes, significantly affecting how relationships and production processes are structured [1].

Industry 4.0, characterized by the integration of advanced technologies that connect the physical and digital worlds, involves the adoption of interconnected and intelligent systems capable of operating autonomously [2]. With the introduction of smart factories, it transforms work organization and the division of tasks between humans, robots, and other advanced technologies [3,4].

Adapting to this new scenario presents significant challenges concerning engineers’ professional competencies, requiring new technical, digital, and behavioral skills. Work environments are being restructured, demanding professionals who can operate flexibly, think critically, collaborate across disciplines, and engage with emerging technologies in an ethically responsible manner [4,5,6,7].

In this context, employability gains prominence as an individual’s ongoing ability to enter, remain, and progress professionally in a volatile, uncertain, complex, and ambiguous (VUCA) labor market [8]. According to reference [9], employability can be understood through four dimensions: human capital (technical skills and knowledge), social capital (relationship networks), individual attributes (behaviors and attitudes), and career development (self-management and professional planning). These elements are becoming central to engineers’ performance in a professional environment shaped by continuous digital transformation.

As digital transformation continues to reshape how work is performed, there is a growing mismatch between the competencies demanded by the industry and those actually delivered by engineers in practice [10]. Many professionals enter the labor market with unclear expectations from employers and significant gaps in their personal, interpersonal, cognitive, and digital skills [11]. Engineering education aims to provide students with a thorough understanding of scientific and engineering principles [9], yet graduates often complete their degrees lacking employability-oriented knowledge and broader professional competencies. The absence of clear and convergent information about labor market expectations leads to the underdevelopment of key skills and misaligned career choices.

In response to new labor market demands, engineers must be both technical experts and humanists. Their employability now depends not only on technical proficiency but also on a wide set of complementary professional skills that enable long-term personal and career development [12].

Although the literature has advanced in analyzing the impact of Industry 4.0 on work and the need for new professional competencies [13,14,15,16,17,18], an important gap remains: few studies provide a refined competency framework, developed with input from engineers and companies, identifying the specific competencies that engineers need to thrive in this new context. Moreover, most existing research focuses on generic skill sets or is limited to specific sectors or countries, lacking a comprehensive model that can inform educational practices, human resources strategies, and professional development policies [19,20,21,22,23,24].

Some studies compare employer expectations with engineering students’ learning outcomes [11,19], while others address lifelong learning in response to new technologies [25,26,27,28]. The work of [28], for example, discusses the strategic role of human resources in responding to such challenges. While these studies explore the need for continuous skill updating and the disconnect between higher education institutions and the labor market, three specific gaps were identified in this research:

Gap 1: Most studies offer broad overviews of market demands but do not structure competencies into organized dimensions.

Gap 2: No prior studies were found that systematically compare employer expectations with the actual competencies delivered by practicing engineers.

Gap 3: The literature lacks studies specifically focused on the employability of engineers in the era of digital transformation, particularly regarding the competencies required throughout their careers.

To address these gaps, the general objective of this study is to propose a career-level competency framework for engineers in the context of digital transformation, developed in three stages. First, a theoretical model was built through a systematic literature review, in which 46 competencies were categorized into seven dimensions—transversal, social, personal, cognitive, digital, green, and technical—(Gap 1). Then, the model was analyzed by practicing engineers to assess the current skillsets they bring to the market (Gap 2). Finally, the framework was refined using input from employers, incorporating expectations for each career level (Gap 3).

Thus, this work answered the following research question: “What are the most relevant skills for engineers in the context of digital transformation throughout their careers?”.

This model aims to support continuous professional development strategies and sustain engineers’ employability over time. By allowing professionals to visualize their strengths and development needs, and helping companies assess the readiness of their talent pool, the framework promotes better alignment between education, practice, and labor market expectations. Its benefits extend to academia and society, fostering self-awareness and a culture of lifelong learning in tune with digital-era demands.

The added value of this article lies in its integration of theory and practice: the framework is grounded in academic research and refined through direct input from the labor market. Unlike previous studies, it differentiates competencies across career levels—junior, mid-level, and senior—capturing how skill demands evolve over time and providing guidance for educational institutions, companies, and professionals involved in engineering training and development.

To guide the reader, the remainder of this paper is structured as follows: Section 2 presents the theoretical foundations related to human capital and engineering competencies in the digital transformation context. Section 3 details the methodological approach, including the mixed-methods design, data collection, and analysis procedures. Section 4 presents the main findings from the literature review, the surveying of engineers, and the interviews with company representatives. Section 5 discusses the results in light of the proposed competency framework and its implications. Finally, Section 6 concludes the study by highlighting its main contributions, limitations, and recommendations for future research.

2. Human Capital and Competency Development

Industry 4.0 has triggered profound changes in human resource management and professional training, demanding restructurings in recruitment, talent development, and retention strategies [29]. Emerging technologies such as artificial intelligence and big data are now widely used for automated resume screening, personalization of selection processes, and predictive performance analysis [30,31]. In parallel, tools such as Virtual Reality, Augmented Reality, and AI-based platforms have been employed to make corporate training more effective, interactive, and personalized [32].

These transformations make the continuous development of the workforce indispensable, with a focus on upskilling and reskilling strategies to adapt to new technological and market demands [7,33,34]. However, the effectiveness of such initiatives depends on cultural and organizational factors: environments that foster empowerment, active participation, and engagement tend to enhance the benefits of these technologies [35]. Thus, investment in human capital is increasingly understood as a fundamental strategic asset for organizational sustainability and innovation, even when it involves operational costs or disruptions [36].

In this scenario, higher education institutions also play a crucial role in training professionals aligned with the demands of Industry 4.0. To fulfill this role, it is necessary to revise curricula, incorporate active learning methodologies, promote interdisciplinarity, and value hands-on experience [13,37]. Competency-based education has emerged as a central model in order to keep pace with labor market transformations and foster sustainable development [21].

The concept of competency has evolved significantly over the past decades and has become central to discussions on professional development, performance management, and employability. A historical examination of the term reveals distinct perspectives shaped by different disciplinary and practical contexts.

The foundational work of [38] defined competencies as the underlying characteristics of an individual that are causally related to effective or superior performance in a job. His behavioral approach emphasized the integration of knowledge, skills, and personal attributes in real work contexts. Subsequently, ref. [39] highlighted the strategic management of competencies as a core element for organizational effectiveness, talent development, and human performance.

Scandinavian and British approaches tend to separate competencies from personal traits, while the American perspective integrates elements such as values, attitudes, and motivations into the concept [40]. Generally, competency is understood as the ability to mobilize knowledge, skills, and personal attributes to act effectively in professional, social, and educational contexts [12].

Reference [41] provides a useful conceptual distinction: knowledge refers to theoretical facts and principles; skills refer to the ability to apply this knowledge in practice; and competency refers to the integrated and contextualized application of both, combined with individual characteristics. Ref. [42] define competencies as a set of knowledge, skills, attitudes, and motivations that enable professionals to face work-related challenges.

It can be observed that, over time, the term has been expanded and refined to reflect the changing nature of work. While some scholars treat skills and competencies as distinct, others use them interchangeably [17,23,39,43]. Given this variation in the literature and the pragmatic focus of this study on identifying and analyzing professional capabilities, we adopt an interchangeable use of “skills” and “competencies” throughout the article, as both are understood as essential attributes for effective performance in engineering roles.

While there is a broad consensus in the literature regarding the importance of competencies in the context of digital transformation, their classification varies significantly across studies [43].

Competencies are most commonly categorized into two broad types: soft skills (interpersonal and behavioral abilities such as communication, empathy, and teamwork) and hard skills (specific technical abilities acquired through formal education) [44].

However, various authors propose alternative classification schemes, grouping competencies into categories such as academic, technical, social, personal, cognitive, and digital skills, or into broader groups like soft and hard skills. Additionally, some frameworks introduce further dimensions—such as methodological, emotional, and green competencies—or domains like employability and transversal skills [13,16,17,20,45].

There is, however, consensus regarding the multidimensional, dynamic, and evolutionary nature of competencies. It is estimated that the average obsolescence time of professional competencies is approximately five years [46], which reinforces the need for continuous updating—especially given the demands of Industry 4.0, which requires professionals who are adaptable, innovative, and capable of lifelong learning [8,21].

3. Materials and Methods

This study adopts a mixed and sequential methodological approach, aligned with its four specific objectives.

A mixed-methods approach combines qualitative and quantitative research techniques to enhance the depth and breadth of analysis. In this study, the sequence follows an exploratory design, where qualitative data inform subsequent quantitative steps [47,48]. The research was carried out in three main stages: (1) a systematic literature review to identify and organize competencies reported in the literature; (2) a quantitative survey with engineers to analyze which competencies are actually being developed and applied in the current job market; (3) a qualitative–quantitative phase of interviews with employers to refine the competency framework across different career levels. This sequential integration allows for triangulation of data and ensures that the resulting framework is both theoretically grounded and empirically validated.

Figure 1 illustrates the methodological procedures adopted.

Figure 1 summarizes the sequential and mixed-methods research design used to develop the competency framework. The systematic literature review (top layer) addresses the first objective—identifying and categorizing competencies in the literature. The validation with engineers (surveys) responds to the second objective, while the validation with companies (interviews) addresses the third. The final objective—to propose a career-level framework—is represented by the integration of findings into the validated model at the bottom of the diagram.

3.1. Theoretical Competency Framework (Objective 1)

To address the first objective—to map and categorize the competencies required of engineers in the digital era—a systematic literature review was conducted, following PRISMA guidelines [49] and the Methodi Ordinatio approach [50].

The SLR process involved formulating research questions, designing a search string using keywords related to “skills,” “engineers,” and “digital transformation,” selecting appropriate databases, and defining inclusion and exclusion criteria.

Among the databases tested, Web of Science and Scopus were selected for this study due to their high volume of publications matching the search terms, the broad accessibility of published material, and their relevance as two of the most comprehensive and recognized databases in the engineering field—incorporating a wide range of indexed sources from multiple publishers.

The inclusion criteria were as follows: (i) peer-reviewed journal articles published between 2014 and 2024; (ii) written in English; (iii) addressing competencies or skills related to engineers or technical professionals in the context of Industry 4.0 and digital transformation.

After the initial retrieval of records, duplicates were removed and abstracts were screened for relevance. A full-text analysis was subsequently conducted on the final set of articles, selected based on their InOrdinatio index score [50]. These articles were examined using MAXQDA 24 content analysis software, which assisted in extracting data on competency types, classification models, and conceptual frameworks. The competencies identified were then categorized into coherent dimensions, considering recurring themes and terminology used across the literature.

The analysis of the selected studies resulted in a theoretical framework composed of 46 distinct competencies, organized into seven dimensions: transversal, social, personal, cognitive, technical, green, and digital. This categorization was intended to overcome the limitations of previous studies, which were often characterized by generic classifications and lacked operational detail regarding the specific skills, behaviors, and knowledge involved.

3.2. Survey of Engineers (To Fulfill Objective 2)

3.2.1. Design

To address the second objective—investigating the competencies currently developed by engineers and validating the theoretical framework from their perspective—a structured survey was developed and administered to engineering professionals. The survey design followed the guidelines proposed by [51], and was based on the theoretical framework of competencies.

The survey administered to engineers was structured into two parts. The first part comprised 13 questions aimed at characterizing the respondent’s profile, while the second consisted of a self-assessment of the 46 mapped competencies and involved a Likert-scale assessment (ranging from 1 to 5). Before arriving at the final version, the questionnaire underwent refinement through multiple meetings among the researchers, during which the terminology, structure, and content of the questions were reviewed.

This approach enabled the analysis of how professionals perceive their own competencies and facilitated the identification of developmental trends throughout different career stages.

3.2.2. Data Collection

The sample of participants in this research was made up of professionals of different levels of seniority (junior, mid-level, and senior), training, and sectors of activity. The criteria for participation were (i) training in engineering and (ii) professional experience in the area at the time of the survey.

The questionnaire was made available via Google Forms between January and March 2025, and was disseminated mainly via social media and professional networks, particularly WhatsApp, Facebook, and LinkedIn. The questionnaire was also shared in groups and forums aimed at engineers.

The sampling process adopted was non-probabilistic for convenience, defined by [52] as being a sample extracted from a source conveniently accessible to the researcher. According to [53], this type of sampling prioritizes practical generalization, that is, ensuring that the knowledge obtained is representative of the population from which the sample was taken, in order to allow inferences from an accessible group.

Ref. [54] highlights that structured surveys are effective methods for testing hypotheses derived from theories and validating theoretical models based on empirical data collected from representative samples. He argues that this type of approach is essential in quantitative studies when the goal is to generalize results and confirm or refine pre-existing theoretical frameworks.

3.2.3. Data Analysis

Although the sample was not random, an estimate of the ideal sample size was calculated based on parameters of simple random sampling in order to provide a reference for evaluating the robustness of the collected sample [55,56]. Considering a 5% margin of error and substituting the values into the standard sample size formula, the minimum estimated sample size would be 385 respondents. Based on this criterion, the study obtained 392 valid responses, which indicates strong sample coverage, even though it does not allow for statistical generalization.

To assess the internal consistency of the 46-item self-assessment scale across junior, full, and senior engineers, Cronbach’s alpha was calculated for each group. The alpha coefficient was consistently 0.93 for all three groups, indicating excellent reliability. The 95% confidence intervals for alpha, estimated by both the Feldt and Duhachek methods, ranged from 0.92 to 0.94 in every case. These findings confirm the robustness and internal coherence of the instrument.

To verify significant differences between respondents’ categorical variables and their self-assessments of competencies across different seniority levels, the Chi-Square Test of Independence (also known as the Test of Association) was applied using RStudio^® [57]. p-values below 0.05 indicated statistically significant differences in perceptions between the analyzed groups. Additionally, Cramér’s V was calculated to complement the analysis by indicating the degree of association between career levels and assessed competencies.

Finally, to understand how competencies within the same dimension relate to one another, perceptual maps and Correspondence Analysis (CA) were generated when applicable, both using RStudio^® version 2024.12.1 [57].

3.3. Interviews with Companies (To Fulfill Objective 3)

3.3.1. Design

The third objective—to validate competency expectations at each career level from the employer’s perspective—was addressed through semi-structured interviews with representatives from companies in industrial sectors, based on [58].

The primary aim of the interviews was to refine the theoretical framework and identify the competencies most in-demand by employers, according to career level (junior, mid-level, or senior). This phase provided a labor market perspective on the skills considered critical for engineers’ professional performance in an increasingly digital and dynamic environment.

The interviews were structured into three parts. The first part aimed to characterize the profile of the respondent and the company they represented. The second part involved a Likert-scale assessment (ranging from 1 to 5) of the relevance of each of the 46 competencies for junior, mid-level, and senior engineers. In the third part, respondents answered a set of open-ended questions concerning their expectations and perceptions regarding the current competencies of employed engineers. Before arriving at the final version of the questions, the structure of the interview was refined [58].

In the semi-structured interview, the scores served to quantify the recruiters’ perception of the level of demand for each competency for each hierarchical level. This numerical assessment was combined with the qualitative analysis of the interviewees’ statements, which allowed for an enriched understanding of the priorities, justifications, and contexts associated with the demand for certain competencies. The qualitative stage of the research provides a perspective from the labor market on the skills considered a priority for professional performance in an increasingly digitalized and dynamic environment.

This triangulation between quantitative and qualitative data strengthens the robustness of the analysis and makes it possible to identify convergences and divergences between the discourse and the practical assessment of the professionals responsible for hiring.

3.3.2. Data Collection

The company representatives who participated in the interviews were selected based on their direct experience in hiring and managing engineers at different hierarchical levels. These professionals held strategic positions, such as Human Resources Managers, Recruitment and Selection Specialists, and Supervisors with direct or indirect responsibility over employees. The interviews were conducted between February and April 2025 via Google Meet and WhatsApp.

3.3.3. Data Analysis

The qualitative sample consisted of 20 interviewees, representing approximately 20% of the companies contacted. In qualitative studies, such as interviews, the focus is on the depth and richness of the information obtained, rather than on statistical representativeness.

According to [59], in semi-structured interviews the number of respondents can be reduced, as long as the participants have qualified knowledge and are inserted in the context investigated. Thus, the choice of this sample is considered adequate to achieve the proposed objectives, allowing the identification of market patterns and expectations regarding the skills required of engineers in the era of digital transformation.

To analyze the competencies that had significant differences between the positions, in the perception of the companies, the Chi-Square Test of Independence and perceptual maps were initially applied. Given the small sample size (n = 20), the Friedman Test—suitable for paired non-parametric data—was also used, followed by Dunn–Bonferroni post hoc correction when necessary [60]. This combined approach ensured greater statistical robustness and reliability in identifying the competencies most valued at each career stage. All statistical tests were conducted using RStudio^®.

To ensure the internal consistency and reliability of the quantitative data collected through the self-assessment questionnaire, Cronbach’s alpha was calculated for each career level (junior, mid-level, and senior) across the 46 evaluated competencies. The results indicated excellent internal consistency at all levels, with alpha coefficients of 0.94 for junior, 0.96 for mid-level, and 0.97 for senior professionals. These values suggest that the competencies measured were highly interrelated and that the instrument used was reliable for assessing engineers’ self-perceptions regarding their skill sets. This reinforces the validity of the subsequent analyses and supports the robustness of the findings regarding skill development throughout career progression.

3.4. Integration of Results and Development of Final Framework (Objective 4)

Based on the triangulation of data from the systematic literature review, quantitative survey, and qualitative interviews, the final Refined Competency Framework by Career Level in Engineering was developed. This model classifies competencies according to professional career stages (junior, mid-level, and senior), serving as a practical tool to guide the continuous professional development of engineers. Moreover, it provides support for educational institutions and employers in aligning training, performance, and expectations within the context of digital transformation.

4. Results and Discussions

4.1. Theoretical Competency Framework

The competencies comprising the theoretical framework were organized into seven dimensions: transversal (4), social (9), personal (14), cognitive (4), digital (7), green (3), and technical (5)—Figure 2.

The classification of competencies by dimensions is essential to understanding the role of each type of skill in the professional performance of engineers, especially in light of the transformations driven by Industry 4.0.

Several studies emphasize that many of these competencies require continuous development, both during academic training and professional practice. According to [43], for example, although digital skills are widely recognized as critical, proficiency levels among engineering students remain moderate to low, particularly in areas such as digital literacy and problem-solving.

In addition to digital and technical competencies, studies such as [45,61] highlight the growing importance of interpersonal and cognitive skills, such as teamwork, continuous learning ability, and effective communication. These competencies are often classified as soft skills, encompassing transversal, personal, social, and cognitive dimensions. They prove fundamental for professional success, as they enable engineers to collaborate, lead, and innovate in increasingly dynamic and complex environments.

In this context, the literature points out that an interdisciplinary approach in STEM education can promote the integrated development of these skills [9,61]. However, development efforts should not be limited to educational institutions. The study by [62], for instance, reveals that one-third of leaders do not guide their teams toward developing new leaders, which highlights the existing gap in internal talent development within organizations.

Therefore, the improvement of engineers’ competencies requires shared responsibility among universities, companies, and society itself. Universities should rethink their curricula to incorporate active methodologies, competency-based teaching, and interdisciplinarity. In turn, companies must strategically invest in the development of their professionals, focusing not only on technical skills but also on human, adaptive, and leadership competencies [11,45].

The theoretical framework proposed here addresses the gap in the literature, which often presents generic and scarcely operational classifications. By proposing a more detailed and dimensional model, this study contributes to building practical and up-to-date references for the development of engineers capable of performing in the digital era. This structure is also consistent with the demands of Industry 4.0, which requires a workforce that is technically prepared but also highly adaptable, collaborative, and strategic [45,63].

4.2. Validation of Theoretical Competency Framework with Engineers

4.2.1. Demography

The sample of 392 participating engineers showed heterogeneity across different levels of professional seniority, as shown in

Table 1. Profile of participating engineers.

Position	Junior	Mid-Level	Senior	Female	Male
Percentage (%)	29.08%	43.37%	27.55%	49.74%	50.26%
Education Level	Bachelor’s Degree	Postgraduate (Lato Sensu)	Master’s Degree	Doctorate	Postdoctoral
Percentage (%)	40.31%	37.24%	15.05%	5.10%	2.30%

.

To verify the existence of a statistically significant association between categorical variables in the sample, the Chi-Square (χ²) Test of Independence was applied to the following variable combinations: gender and age, gender and salary, age and salary, position and salary, position and gender, and position and age, as shown in

Table 2. Chi-Square Test of Independence for sample profile.

Variable Combination	p-Value
Gender and age	0.002
Gender and salary	0
Age and salary	0
Position and salary	0
Position and gender	0.037
Position and age	0

.

The results presented in Table 2 indicate that all associations between the analyzed variables were statistically significant. Therefore, with the aim of jointly exploring the association patterns among respondent profiles, a Multiple Correspondence Analysis (MCA) was conducted to investigate these patterns [57] (Figure 3).

The biplot generated by the Multiple Correspondence Analysis (MCA), shown in Figure 3, presents individuals (gray numbers) distributed according to their response categories and grouped by hierarchical level (junior, mid-level, and senior).

The visualization reveals three well-defined clusters:

• The junior-level professionals cluster (in green) is positioned in the upper-left quadrant of the plot and tends to be associated with characteristics such as age between 21 and 25 years, salary below BRL 3000 or between BRL 3000 and BRL 5000, and work experience between 1 and 5 years.
• The mid-level professionals cluster (in orange) is concentrated in the central region of the plot, associated with intermediate salary ranges (between BRL 5000 and BRL 8000), work experience between 6 and 10 years, and ages between 26–29 and 30–35 years. There is also a gender balance within this group.
• The senior-level professionals cluster (in blue) is located in the upper-right quadrant, linked to categories such as salaries above BRL 12,000, more than 10 years of professional experience, and ages over 40. This group also shows a higher concentration of male respondents.

The distribution of categories highlights clear career progression patterns, with higher levels associated with increased age, work experience, and compensation—reinforcing the internal consistency of the data. Additionally, the moderate overlap between gender and professional level suggests that, although there is some symmetry, structural differences in professional profiles between men and women persist, especially at the higher salary levels.

4.2.2. Engineers’ Perception of Their Competencies

This stage aimed to assess which competencies appear to be more consolidated or deficient throughout the professional trajectory, as well as to identify evolutionary patterns across junior, mid-level, and senior positions. This practical validation is essential to ensure the applicability of the proposed model in real-world contexts of engineering education and professional development.

Table 3. Chi-Square Test of Independence—engineers.

Dimensions	Competencies	p-Value	Cramér’s V
Transversal Skills	Continuous and Adaptive Learning	0.547	0.077
	Integrated Knowledge	0.003	0.158
	Management and Coordination	0	0.179
	Visionary	0	0.194
Social Skills	Interpersonal Influence	0.008	0.157
	Teamwork	0.011	0.158
	Leadership	0.004	0.179
	Mentoring	0.097	0.125
	Communication	0.029	0.131
	Language Skills	0.584	0.092
	Feedback Management	0.028	0.414
	Cultural Awareness	0.366	0.093
	Networking	0.362	0.103
Personal Skills	Professionalism	0.105	0.123
	Multitasking	0.02	0.144
	Persistence	0.012	0.157
	Curiosity	0.133	0.105
	Authenticity	0.429	0.089
	Excellence and Growth Mindset	0.391	0.084
	Agreeableness	0.022	0.132
	Independence	0.01	0.156
	Resilience	0.653	0.072
	Organization	0.496	0.1
	Emotional Intelligence	0.524	0.106
	Intuition	0.202	0.114
	Proactivity	0.009	0.146
	Reliability	0.017	0.131
Cognitive Skills	Logical Reasoning	0.036	0.138
	Problem-Solving and Decision-Making	0.038	0.128
	Strategic Thinking	0.2	0.107
	Creative and Innovative Thinking	0	0.195
Digital Skills	Programming and Coding	0.358	0.106
	Systems and Networking Competence	0.961	0.059
	Human–Machine Interfaces	0.566	0.093
	IT Security and Data Protection	0.134	0.123
	Digital Communication and Marketing	0.051	0.14
	Data Analysis and Management	0.045	0.142
	Digital Literacy/Computational Thinking	0.025	0.15
Green Skills	Sustainable Design	0.166	0.121
	Sustainable Thinking	0.483	0.098
	Social Responsibility	0.802	0.076
Technical Skills	Research Skills	0.001	0.173
	Understanding of Processes	0.001	0.001
	Knowledge of IT and Production Technologies	0.002	0.171
	Knowledge in Exact Sciences	0.043	0.143
	Know-How	0	0.208

presents the results of the Chi-Square Test of Independence.

Based on the study in [64], it is evident that the development of competencies throughout engineers’ careers does not follow a single standardized pattern, being influenced by various contextual, educational, and professional factors. The authors highlight that the lack of consensus on which knowledge, skills, attitudes, and experiences constitute a global competency has resulted in heterogeneous training interventions and, in many cases, certification programs lacking robust scientific foundations.

This observation reinforces the findings presented in Table 3, which identified statistically significant differences in the mastery of 24 competencies among engineers at different hierarchical levels (junior, mid-level, and senior). These differences suggest that competencies develop progressively and unevenly, reflecting the accumulation of practical experience, the assumption of responsibilities, and the organizational contexts encountered throughout one’s career. These data underscore the need for more structured and evidence-based training strategies that recognize the evolution of competencies according to career stage.

Table 4. Competencies developed by career level.

Dimensions	Competencies	Junior	Mid-Level	Senior
Transversal Skills	Continuous and Adaptive Learning *	++	++	++
	Integrated Knowledge	±	+	++
	Management and Coordination	-	+	++
	Visionary	±	+	++
Social Skills	Interpersonal Influence	±	+	++
	Teamwork	-	+	++
	Leadership	±	+	++
	Mentoring *	++	++	++
	Communication	±	+	++
	Language Skills *	-	-	-
	Feedback Management	±	++	+
	Cultural Awareness *	++	++	++
	Networking *	+	+	+
Personal Skills	Professionalism *	++	++	++
	Multitasking	±	+	++
	Persistence	+	+	++
	Curiosity *	++	++	++
	Authenticity *	++	++	++
	Excellence and Growth Mindset *	++	++	++
	Agreeableness	++	++	+
	Independence	±	+	++
	Resilience *	+	+	+
	Organization *	+	+	+
	Emotional Intelligence *	+	+	+
	Intuition *	+	+	+
	Proactivity	+	++	++
	Reliability	+	±	++
Cognitive Skills	Logical Reasoning	++	+	+
	Problem-Solving and Decision-Making	±	+	++
	Strategic Thinking *	+	+	+
	Creative and Innovative Thinking	±	++	++
Digital Skills	Programming and Coding *	-	-	-
	Systems and Networking Competence *	-	-	-
	Human–Machine Interfaces *	-	-	-
	IT Security and Data Protection *	-	-	-
	Digital Communication and Marketing *	±	±	±
	Data Analysis and Management	-	++	+
	Digital Literacy/Computational Thinking	-	+	++
Green Skills	Sustainable Design *	-	-	-
	Sustainable Thinking *	+	+	+
	Social Responsibility *	++	++	++
Technical Skills	Research Skills	-	+	++
	Understanding of Processes	±	++	+
	Knowledge of IT and Production Technologies	-	+	++
	Knowledge in Exact Sciences	-	++	+
	Know-How	-	+	++

* no significant difference between career levels.

summarizes the degree of competency development among engineers at the junior, mid-level, and senior stages, based on the questionnaire results and perceptual maps generated in RStudio^® for the 24 competencies that showed significant differences.

The prominence of each competency at each career level was classified into four categories: ++ (highly developed), + (developed), ± (moderately developed), and - (underdeveloped).

According to Table 4, junior, mid-level, and senior engineers all exhibit highly developed competencies in Continuous and Adaptive Learning, Mentoring, Cultural Awareness, Professionalism, Curiosity, Authenticity, Excellence and Growth Mindset, and Social Responsibility. In addition, they demonstrate good development in competencies such as Resilience, Organization, Emotional Intelligence, Intuition, Strategic Thinking, and Sustainable Thinking, although there is still room for improvement in these areas.

On the other hand, competencies related to Digital Communication and Marketing are classified as moderately developed across all three career levels. Meanwhile, skills in Languages, Programming and Coding, Systems and Networks Competency, Human–Machine Interfaces, IT Security and Data Protection, and Sustainable Design are shown to be underdeveloped or largely lacking among the professionals analyzed.

This scenario highlights a notable deficiency in digital competencies among engineers, pointing to a significant gap in areas that are increasingly critical for digital transformation. This finding is consistent with [65], which emphasizes that digital skills are no longer optional but have become critical competencies for contemporary professional practice. Furthermore, ref. [65] emphasizes that these skills must be developed in conjunction with social competencies, forming an integrated set that prepares professionals for the complex and interdisciplinary challenges of the digital age.

When evaluating the competencies that showed statistically significant differences, early-career professionals (junior engineers) stood out, particularly in essential personal and cognitive competencies.

Like junior engineers, mid-level engineers also exhibited a lack of digital skills. The most developed competencies were predominantly found among senior engineers, although their areas for improvement mirrored those of the mid-level group.

The analyzed data reveal a consistent pattern of competency evolution throughout the engineers’ career paths, with a clear progression in the development of transversal, social, and cognitive competencies as they advance in their careers. While junior engineers stand out for fundamental personal and cognitive traits, mid-level and senior engineers expand this repertoire, demonstrating growing mastery in transversal and social competencies.

However, a recurring and concerning finding across all experience levels is the low proficiency in digital competencies—particularly in programming, information security, and systems and networks. This gap represents a significant limitation in meeting the demands of digital transformation, regardless of career level.

The study in [66] supports this concern, highlighting a strong demand for both basic and advanced IT skills for effectively performing tasks in today’s work environment. This reinforces the notion that mastering such competencies is no longer optional but has become a fundamental requirement for professional performance.

Figure 4 presents a heatmap summarizing how engineers self-assessed their performance across the seven mapped competency dimensions. The cooler colors and positions toward the outer regions of the heatmap indicate more developed dimensions, while warmer colors, with competencies located closer to the center, represent less developed dimensions.

4.2.3. Analysis of Relationship Between Engineers’ Competencies

To analyze whether there is a relationship between the engineers’ skills, Multiple Correspondence Analysis (MCA) was carried out for each dimension of the framework. For transversal competencies, evidence of strong associations between them was found.

It is noticeable that when an engineer demonstrates low levels of Management and Coordination, they also tend to show poor Continuous and Adaptive Learning, limited Integrated Knowledge, and a lack of Visionary thinking. The opposite is also true.

In the MCA of social competencies, the competencies rated with the highest score (5)—such as Leadership, Networking, Teamwork, Interpersonal Influence, Feedback Management, Mentoring, Communication, and Cultural Awareness—appeared grouped and located near the senior level on the plot. This suggests that these skills are commonly associated with more experienced professionals and those in higher-responsibility positions.

Conversely, lower scores for competencies such as Teamwork, Mentoring, Networking, and Interpersonal Influence were strongly isolated on the far left side of the plot. This reinforces the idea that underdevelopment in these areas tends to concentrate within a specific group of individuals.

The MCA for personal competencies indicated that professionals with low Persistence also tend to show low Independence. Likewise, when low scores are given for Agreeableness, they are also associated with low levels of Trustworthiness and Professionalism. A low rating in Excellence and Growth Mindset is also linked to low levels of Curiosity and Resilience.

Junior-level professionals exhibited intermediate characteristics and were more dispersed across the plot, indicating a diversity of profiles. More experienced profiles, on the other hand, were strongly associated with high levels of behavioral competencies. These findings are consistent with the work of [64].

The MCA for cognitive competencies revealed that engineers struggling with Problem-Solving and Decision-Making were also associated with low levels of Logical Reasoning and Strategic Thinking.

The MCA plot for digital competencies showed a clear relationship among them. Junior, mid-level, and senior positions appeared near the center of the plot, adjacent to intermediate competency levels (levels 2 to 4). This suggests a continuous progression between digital competency development and career advancement, as also evidenced in Table 4. This configuration reinforces the notion that the development of digital competencies is a key competitive advantage for career growth.

The MCA plot for green competencies revealed a strong association among the three sustainability-related competencies, indicating a high level of homogeneity in responses. However, the competency Sustainable Design appeared somewhat distant from the others, suggesting that it remains less consolidated among the surveyed engineers. In other words, when an individual scores 4 on Social Responsibility and Sustainable Thinking, they tend to rate slightly lower on Sustainable Design.

Low ratings across all three green competencies also appeared closely grouped, indicating that a lack in one of these areas is typically accompanied by deficiencies in the others.

Finally, the MCA for technical competencies confirmed what had already been inferred from previous analyses: individuals tend to evaluate themselves similarly across all competencies within this dimension.

4.3. The Validation of the Theoretical Framework from a Business Perspective

Table 5. Competencies with statistically significant differences according to companies.

Dimension	Competence	Chi-Square Test of Independence	Friedman Test
Transversal Skills	Continuous and Adaptive Learning *	0.703	0.49500000
	Integrated Knowledge	0	0.00000304
	Management and Coordination	0	0.00000011
	Visionary	0	0.00005800
Social Skills	Interpersonal Influence *	0.847	0.60700000
	Teamwork	0	0.00000013
	Leadership	0.843	0.00000011
	Mentoring	0	0.00000604
	Communication	0	0.00000857
	Language Skills	0.008	0.00000417
	Feedback Management	0.005	0.00006920
	Cultural Awareness *	0.299	0.00959000
	Networking	0.012	0.00042000
Personal Skills	Professionalism *	0.795	0.08210000
	Multitasking	0.001	0.00038400
	Persistence	0	0.00002240
	Curiosity *	0.161	0.69800000
	Authenticity *	0.875	0.08630000
	Excellence and Growth Mindset *	0.495	0.24900000
	Agreeableness *	0.969	0.86700000
	Independence	0	0.00000007
	Resilience	0	0.00029900
	Organization	0	0.00000076
	Emotional Intelligence	0.001	0.00000912
	Intuition	0	0.00000035
	Proactivity	0	0.00000326
	Reliability *	0.667	0.03660000
Cognitive Skills	Logical Reasoning	0	0.00000226
	Problem-Solving and Decision-Making	0	0.00000034
	Strategic Thinking	0	0.00000090
	Creative and Innovative Thinking	0.009	0.00138000
Digital Skills	Programming and Coding	0	0.00042800
	Systems and Networking Competence	0.018	0.00001280
	Human–Machine Interfaces *	0.203	0.00734000
	IT Security and Data Protection *	0.437	0.00854000
	Digital Communication and Marketing *	0.723	0.04780000
	Data Analysis and Management	0.04	0.00003100
	Digital Literacy/Computational Thinking	0.008	0.00091200
Green Skills	Sustainable Design	0.001	0.00000529
	Sustainable Thinking	0	0.00000529
	Social Responsibility *	0.061	0.00091200
Technical Skills	Research Skills	0	0.00000134
	Understanding of Processes	0	0.00000060
	Knowledge of IT and Production Technologies	0	0.00000383
	Knowledge in Exact Sciences	0	0.00000799
	Know-How	0	0.00000023

* no significant difference between career levels.

presents, from the companies’ perspective, the competencies that show statistically significant differences among groups of engineers, according to the Chi-Square Test of Independence and the Friedman Test.

Upon analyzing Table 5, it is observed that most of the competencies that showed statistically significant differences across job levels in the statistical tests were consistent. However, some competencies demonstrated significant differences only in the Friedman Test and not in the Chi-Square Test of Independence. These include Social Responsibility, Human–Machine Interfaces, IT Security and Data Protection, Digital Communication and Marketing, Reliability, Cultural Awareness, and Leadership. This divergence is expected, as the Friedman Test is more sensitive to variations in small samples.

However, when applying the Dunn post hoc test with Bonferroni correction to the competencies that showed a p-value lower than 0.05 in the Friedman Test, only Leadership remained statistically significant after the correction.

Thus, 33 competencies were considered to show significant differences in requirements across job levels (junior, mid-level, and senior) according to human resources professionals. This finding aligns with the study by [67], in which the authors analyzed thousands of engineering job postings and demonstrated that the demand for professional skills varies substantially according to education level, engineering specialization, and salary range. This variation indicates that the job market clearly differentiates the expected profiles for each career stage, adjusting its requirements according to the complexity of responsibilities and level of autonomy expected.

This reinforces the notion that competencies are not uniformly distributed among engineers but are instead shaped by factors such as education, experience, and responsibilities assigned throughout career progression. Therefore, the differences observed across job levels also reflect market expectations regarding the technical, behavioral, and strategic maturity of professionals at each stage of their careers.

Accordingly, 33 competencies were identified as significantly different, and to understand how these differences manifest between junior, mid-level, and senior positions, both perceptual maps and the adjusted p-values from the Dunn–Bonferroni test were analyzed. The results can be seen in

Table 6. Labor market competency expectations.

Dimensions	Competencies	Junior	Mid-Level	Senior
Transversal Skills	Continuous and Adaptive Learning *	+	+	+
	Integrated Knowledge	±	+	++
	Management and Coordination	±	+	++
	Visionary	±	+	++
Social Skills	Interpersonal Influence *	++	++	++
	Teamwork		+	++
	Leadership		+	++
	Mentoring	±	++	++
	Communication	±	+	++
	Language Skills		+	++
	Feedback Management	+	++	++
	Cultural Awareness *	++	++	++
	Networking	+	++	++
Personal Skills	Professionalism *	++	++	++
	Multitasking	+	++	++
	Persistence	+	++	++
	Curiosity *	++	++	++
	Authenticity *	+	+	+
	Excellence and Growth Mindset *	+	+	+
	Agreeableness *	++	++	++
	Independence	±	+	++
	Resilience	+	++	++
	Organization	+	+	++
	Emotional Intelligence	+	++	++
	Intuition	±	+	++
	Proactivity	+	++	++
	Reliability *	++	++	++
Cognitive Skills	Logical Reasoning	+	++	++
	Problem-Solving and Decision-Making	+	++	++
	Strategic Thinking	+	++	++
	Creative and Innovative Thinking	+	++	++
Digital Skills	Programming and Coding	±	+	++
	Systems and Networking Competence	±	±	+
	Human–Machine Interfaces *	+	+	+
	IT Security and Data Protection *	±	±	±
	Digital Communication and Marketing *	+	+	+
	Data Analysis and Management	±	++	++
	Digital Literacy/Computational Thinking	±	++	++
Green Skills	Sustainable Design	±	+	++
	Sustainable Thinking	±	+	++
	Social Responsibility *	+	+	+
Technical Skills	Research Skills	±	+	++
	Understanding of Processes	±	+	++
	Knowledge of IT and Production Technologies	±	+	++
	Knowledge in Exact Sciences	+	++	++
	Know-How	±	+	++

* no significant difference between career levels.

.

Table 6 presents a summary of the labor market’s perception regarding the demand for specific competencies in engineers, according to hierarchical levels (junior, mid-level, and senior). A symbol system was used to represent the degree of expectation companies have for each competency by level:

(++) indicates a high demand for that competency at the respective position.

(+) represents a moderate demand.

(±) signals an expectation of development.

Blank cells, specially highlighted in red, indicate that the market does not expect professionals at that hierarchical level to demonstrate significant mastery or performance in the given competency.

It is noticeable in Table 5 that most personal and cognitive competencies are equally demanded by HR for all three levels: junior, mid-level, and senior. This finding is supported by [66,67], who identified skills such as taking initiative, determining what needs to be done in the absence of guidance, planning and resource management, openness to ideas, creativity, innovative thinking, independence, proper application of knowledge, continuous learning, process orientation, ethical decision-making, and professionalism as some of the “fundamental business knowledge and skills” necessary for current professionals regardless of experience.

When assessing the digital, green, and technical competency dimensions in Table 5, it becomes clear that these are demanded differently according to the professional’s hierarchical level, indicating a progression expected by HR as engineers advance in their careers. This trend is also observed in certain social and transversal competencies, which gain relevance at higher levels of responsibility and professional autonomy.

This differentiation is further supported by [67], who found that technical and digital competencies—such as those related to Sciences and Systems Analysis—are more prevalent in job postings requiring higher educational levels, suggesting that these skills are strongly associated with greater technical complexity and responsibility, typical of more senior positions.

Thus, the results of this research reinforce that although some competencies are essential at all career stages, others become crucial only with professional advancement, especially those involving specialized technical mastery and strategic vision.

From the interview results, it is possible to infer that recruiters indicate a greater demand for cognitive and personal competencies at the junior level. Essential competencies for this position include Teamwork, Cultural Awareness, Professionalism, Authenticity, Agreeableness, and Reliability. Although prior experience is not mandatory, internships or practical experience in the field are valued during hiring for junior engineers.

For the mid-level position, the labor market generally expects solid development across all mapped competencies, with an emphasis on cognitive, personal, and social skills. Open-ended responses from interviewees about hiring criteria for mid-level engineers highlight higher expectations regarding professional experience and autonomy level. Mid-level professionals are expected to have solid technical knowledge and practical experience, along with decision-making ability, problem-solving skills, critical data analysis, Interpersonal Influence, and innovation. Resilience, emotional intelligence, professional maturity, a proactive attitude, an emerging leadership profile, and independence were frequently mentioned as well.

In summary, the mid-level role demands a balance between advanced technical knowledge, interpersonal and social skills, and initial management involvement, reflecting an intermediate stage of professional and organizational development.

For the senior level, according to Table 5, nearly all competencies are highly desirable. Interview responses for senior engineer positions emphasize strong management and leadership skills combined with a solid technical foundation and extensive professional experience. Seniors are expected not only to manage teams, processes, and projects but also to take on strategic responsibilities, develop solutions to complex problems, perform persuasive leadership, mentor, manage feedback, exhibit resilience, and demonstrate professional maturity. In essence, the senior engineer should serve as both a technical and managerial reference, capable of leading with strategic vision and developing new professionals.

Figure 5 provides a visual summary of the competency dimensions most valued by recruiters at each job level: junior, mid-level, and senior engineers.

It is important to highlight that, although the figure presents the dimensions generally, the detailed analysis of each competency revealed internal variations: some specific competencies within the same dimension were more or less desired depending on the job level. Thus, the figure serves as a general synthesis of preferences by dimension but does not replace the detailed individual competency analysis.

As in Figure 4, cooler colors and positions toward the outer regions of the heatmap indicate more developed dimensions, while warmer colors, with competencies located closer to the center, represent less developed dimensions.

During the interviews, all respondents (100%) confirmed that the mapped competencies fully encompass the demands of the labor market, with no indication of additional competencies perceived as missing. Furthermore, 75% of the interviewees acknowledged that third-party recommendations can influence hiring decisions, highlighting the importance of strong networking regardless of the position.

When asked about their level of satisfaction with their currently hired engineers, most respondents reported a high satisfaction level (between 4 and 5). There were few lower ratings (2 or 3), indicating overall good performance in hiring from the HR perspective, with an average rating of 3.83.

4.4. Competency Framework Refined by Career Level

Based on the convergence of data from the literature, professionals’ self-assessments, and recruiters’ perceptions regarding the 46 mapped competencies, a structural competency model for engineers was proposed, organized into three levels of professional maturity: junior, mid-level, and senior. The model aims to reflect the expected progression in competency development throughout a career, aligning with the demands of digital transformation and the increasing complexity of responsibilities at each hierarchical level.

The quantitative and qualitative results revealed a clear evolution of competencies along engineers’ professional trajectories, as previously predicted by [66,67]. The data cross-analysis demonstrated not only the technical and behavioral progression of professionals but also highlighted the most sensitive gaps from the market perspective.

The analysis of self-assessments and interviews indicates that, for early-career engineers, there is a strong emphasis on basic personal and cognitive competencies. However, it is noticeable that junior-level engineers currently available in the market still need to improve certain skills requested by recruiters, such as Teamwork, Reliability, Problem-Solving and Decision-Making, Creative and Innovative Thinking, Human–Machine Interfaces, Digital Communication and Marketing, and mathematical knowledge. Additionally, most digital and technical competencies require development.

In this context, previous studies have examined the impact of implementing enabling technologies—such as the Internet of Things (IoT) and Virtual Reality (VR)—to enhance learning and development programs. These advanced technologies have demonstrated their potential to improve human capabilities in problem-solving by strengthening relevant skills, behaviors, and knowledge. Thus, the integration of such technologies into professional development strategies may help accelerate the readiness of junior engineers for the demands of the digital era [68].

Thus, the Junior engineer is understood as a professional in a phase of practical training, characterized by consistent behavioral traits and adaptability. This perspective is reinforced by [69], who point out that the competencies of early-career engineers tend to vary depending on the professional roles they occupy, contributing to greater heterogeneity in competencies observed among juniors. This diversity reflects not only the specific demands of each organizational context but also the different technical and interpersonal development paths these professionals may follow in the initial stages of their careers.

For mid-level engineers, the data converge toward a significant appreciation of cognitive, personal, and social competencies. From the market’s point of view, mid-level professionals still need improvement in competencies such as Networking, Multitasking, Persistence, Resilience, Emotional Intelligence, Logical Reasoning, Problem-Solving and Decision-Making, Strategic Thinking, and Digital Literacy. The expectation is that these engineers begin to act as references for younger colleagues, also developing competencies related to emerging leadership [70].

This expectation is further reinforced by recent findings indicating that Mid-level Managerial Competencies (MMCs) have a significant and direct positive impact on a company’s intention to adopt Industry 4.0 technologies. As MMCs increase, so does the organization’s willingness to embrace digital transformation. Notably, this relationship is not mediated by Industry 4.0 readiness or perceived usefulness, underscoring that MMCs exert an independent influence on digital adoption decisions. These insights highlight the crucial role of mid-level professionals in driving technological change, emphasizing the need to strengthen their leadership and strategic competencies to ensure a smooth and successful transition to Industry 4.0 [70].

At the top of professional maturity, senior engineers are perceived as reference professionals who accumulate experience across the seven mapped dimensions. The competencies most emphasized at this level, compared to mid-level engineers, include Integrated Knowledge, Management and Coordination, Visionary Thinking, Interpersonal Influence, Leadership, Communication, Language Skills, Independence, Organization, Intuition, Programming and Coding, Sustainable Design, Sustainable Thinking, Research Skills, Process Understanding, IT and Production Technologies Knowledge, and Know-How.

This set of competencies aligns closely with those typically attributed to top management, who are instrumental in promoting a culture of innovation and enabling technological advancement within organizations. Senior professionals, particularly those in leadership or strategic roles, play a key part in fostering an environment conducive to experimentation, reducing resistance to change, and facilitating the successful adoption of Industry 4.0 initiatives. Their ability to influence decision-making, allocate resources, and guide interdisciplinary teams reflects the essential contribution of Top Management Team Competencies (TMTCs) in driving digital transformation and supporting long-term organizational readiness for Industry 4.0 [70].

Regarding the senior engineers available in the market, there is a lack of Language Skills, Organization, Intuition, and especially digital competencies. Both self-assessments and recruiters’ perceptions indicate that team management, the development of complex solutions, and the ability to influence and technically guide others are indispensable aspects at this level. There is also a clear expectation of mastery over advanced technological tools and performance indicators, although, in practice, some senior engineers also show limitations in digital skills.

The comparison between the two approaches—engineers’ self-assessment and recruiters’ (companies’) perception—confirmed that the 46 mapped competencies are sufficient to cover current market demands, with no need for additions.

However, a structural gap became evident: digital competencies were systematically assessed as underdeveloped across all levels. This deficit was pointed out by both engineers and companies, revealing a concerning mismatch with the demands of digital transformation. This finding becomes even more critical given the scenario described by [66], who highlight that the use of digital skills has become an integral part of daily professional activities and is determinant for organizational success. Furthermore, the lack of digital proficiency may entail significant risks to information security, especially when employees access networks or devices without proper protections, exposing sensitive company and client data.

The proposed structural model in Figure 6 suggests a linear and multidimensional evolution of competencies, with an initial emphasis on personal and cognitive skills, technical and social maturation for mid-level engineers, and strategic and leadership integration at the senior level. The model recommends the intentional and progressive incorporation of digital competencies throughout the entire career path as a fundamental transversal axis for engineers’ performance in the Industry 4.0 context, as already stated by [65,66].

One of the main differentiators of this research lies in the integration of the engineers’ perspective with the views of human resources professionals and managers responsible for their hiring. While much of the existing literature on competencies relies on theoretical analyses or the perception of academic experts, this study sought to directly engage the market—both from the standpoint of those occupying engineering roles and those defining the hiring criteria.

This approach added robustness and practical relevance to the findings, enabling the validation of identified competencies not only conceptually but also in their real application within organizational settings. Thus, the study significantly contributes to aligning education, professional practice, and market expectations, fostering a more accurate understanding of industry demands and providing concrete guidance for educational institutions, professional development programs, and capacity-building policies targeting engineering in the digital transformation era.

5. Limitations

This research has some limitations that should be considered when interpreting the results. The engineer sample was obtained through convenience sampling, which limits the generalizability of the findings [71]. Furthermore, the use of self-assessment may have introduced perceptual biases influenced by subjective factors such as self-confidence or prior experiences.

The interviews with companies, although valuable for capturing sector demands, were also based on non-probabilistic sampling and faced logistical challenges regarding access and participation. The non-probabilistic nature of the sample requires caution when generalizing results, as perceptions are conditioned by the specific experiences and organizational contexts of the respondents [52,72].

Another limitation relates to terminological and interpretative variability regarding the competencies assessed. Despite efforts to clearly define and standardize concepts in the questionnaire and interviews, it is natural that differences in understanding exist for certain terms, especially among respondents from diverse educational backgrounds, industry segments, and geographic regions.

6. Conclusions

This study sought to answer the following research question: “What are the most relevant competencies for engineers in the context of digital transformation throughout their careers?” To this end, a three-phase approach was adopted, combining a systematic literature review, self-assessments by practicing engineers, and insights from employers. The resulting framework captures the progression of competencies across career stages and reflects how skill demands evolve from junior to senior levels. While previous studies such as [65,66,67] have acknowledged that engineering competencies develop progressively throughout one’s career, they did not specify how these skills evolved—this article addresses that gap by proposing the Competency Framework by Career Level, offering a structured and empirically grounded model to guide professional development in the digital era.

First, the findings indicate that junior engineers are expected to demonstrate strong personal and cognitive competencies. However, both practitioners and recruiters highlighted significant deficiencies in digital readiness and professional maturity at this stage. These observations are in line with previous research that has emphasized the persistent misalignment between graduate profiles and industry expectations [22,23,66], reinforcing the need to strengthen employability-oriented training in engineering education [9,21,67].

Second, for mid-level engineers, the focus shifts toward the consolidation of technical and social competencies. This supports the existing literature that underscores the critical role of mid-level professionals in advancing digital transformation, where behavioral agility becomes essential to navigating complexity and change. The contribution of this study lies in its identification of enduring skill gaps that often persist despite several years of experience—an area underexplored in previous work [25,26,70].

Third, senior engineers are positioned as reference professionals, expected to integrate advanced technical, digital, and transversal competencies while also assuming roles as mentors and strategic leaders. The present findings build upon and extend existing competencies lists [10,11,12,13,14,15,16,17,18,19,20,21] by anchoring them in empirical data validated by the labor market, rather than relying solely on theoretical constructs.

A particularly significant finding was the identification of a persistent and substantial deficit in digital competencies—including programming, information security, and data analysis—across all professional levels, indicating a critical structural gap in light of Industry 4.0 demands [45,65,66]. This underscores the urgent need for curriculum revision in higher education, as well as ongoing training programs within organizations [13,21,29,32,37,68].

In terms of its contribution, this study offers distinct value by integrating competency categorization and career progression—validated by market input—into a single, comprehensive framework. By triangulating multiple data sources, the research presents an applied and refined model that bridges theoretical and practical perspectives.

Among the study’s limitations, the use of non-probabilistic convenience samples in both the questionnaire and company interviews restricts the generalizability of results. Additionally, the self-assessment nature of the survey may have introduced perception biases.

For future research, it is recommended to deepen methodological triangulation, expand the sample to include different industrial sectors and regional contexts, and conduct longitudinal studies that track competency evolution over time. The proposed model offers a strategic tool to support the sustainable professional development of engineers, promoting alignment between education, market needs, and the contemporary demands of digital engineering.