An extensive analysis of 26,103 engineering job advertisements offers a detailed view of the technical and professional skills most valued by employers, and how these demands vary by discipline, education level, and salary. Using the U.S. Department of Labor’s O*NET skills database, the study identified the prevalence of specific competencies across biomedical, civil, chemical, electrical, environmental, and mechanical engineering roles requiring bachelor’s, master’s, or doctoral degrees.
Across all education levels, problem-solving emerged as the most frequently cited professional skill, appearing in more than half of postings. Communication and teamwork elements, such as coordination, were also common. Graduate-level positions more often sought math, science, and systems analysis capabilities, indicating heightened expectations for analytical and domain-specific expertise. Technical skill requirements showed Microsoft Excel as the most consistently requested tool, appearing in roughly 40% of postings, with AutoCAD prominent in civil engineering and SolidWorks favored in biomedical and mechanical roles. For PhD-level positions, MATLAB and Python were more frequently specified than in bachelor’s-level postings.
Disciplinary differences were evident. Civil engineering postings emphasized coordination more than other fields, reflecting the collaborative demands of large-scale infrastructure projects. Electrical engineering roles often included PLC programming and CAD software, while biomedical engineering postings highlighted FMEA, MATLAB, and Python. Environmental engineering roles showed moderate demand for AutoCAD and ArcGIS, with less emphasis on programming skills.
Salary data, available in 93% of postings, revealed clear education-linked differences. Median salaries rose from $80,000 for bachelor’s-level roles to $87,000 for master’s-level and $101,500 for PhD-level positions. Discipline-specific medians for bachelor’s roles ranged from $76,000 in civil engineering to $84,500 in biomedical engineering. These figures align with broader labor statistics, underscoring the financial return associated with advanced degrees and certain fields.
A notable contribution of the study is the concept of “skill premiums”—the median salary difference between jobs listing a specific skill and those that do not. For bachelor’s-level positions, Python carried a $14,750 premium, and MATLAB $14,000, while ubiquitous tools like Excel and AutoCAD often had negative premiums, suggesting they are baseline expectations rather than differentiators. Professional skills with higher premiums included math, systems analysis, science, and systems evaluation. Communications-related skills, despite their frequency, tended to have lower premiums, reflecting their status as fundamental requirements across roles.
By disaggregating data by discipline, the analysis revealed that skill premiums vary significantly. MATLAB skills in biomedical engineering postings corresponded to a $19,000 premium, while in chemical engineering the premium was $4,000. Such disparities highlight the importance of aligning skill development with the specific demands and compensation patterns of each field.
The findings resonate with the World Bank’s Systems Approach for Better Education Results in Workforce Development framework, which emphasizes the alignment of skills supply from education systems with industry demand. Historically, engineering education research has focused on the supply side, often guided by accreditation criteria or visionary reports like the National Academy of Engineering’s *Engineer of 2020*. This study’s demand-side approach, grounded in large-scale empirical data, offers fresh insight into current workforce needs.
For educators and program designers, the results suggest the value of tailoring curricula to reflect the nuanced skill demands across education levels and disciplines. Embedding high-premium technical skills such as Python and MATLAB into coursework could enhance graduates’ competitiveness and earning potential. For students, understanding which skills command higher salaries provides a data-driven basis for prioritizing learning efforts, especially in elective courses or independent projects.
The methodology—leveraging natural language processing for large-scale text analysis—also represents a significant advance over prior studies with smaller, regional samples. While limited to a single job site and a snapshot in time, the dataset’s size and scope enable population-level insights that can inform both policy and practice in engineering education. The connection between skill frequency, discipline-specific demand, and salary premiums offers a richer picture of the labor market than frequency analysis alone, making it a valuable resource for those seeking to navigate or shape the future engineering workforce.