2025 Data Engineering Demographic Insights

Compare 2025 Data Engineering compensation data and salaries across geographies, genders and industries.

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‍Geographic Trends in Data Engineering Compensation:

Regional trends continue to influence Data Engineer and Manager pay in 2025, reflecting differences in living costs, AI investment, and enterprise data maturity across U.S. markets. While the Northeast and West Coast stay as salary leaders, emerging tech hubs in the Midwest, Southeast, and Mountain regions are narrowing the gap—offering competitive wages along with more affordable living costs.

  • Northeast leads compensation, maintaining the highest averages across both IC and Manager levels, particularly at the MG-3 tier, where median pay exceeds $260K.
  • West Coast salaries remain strong but show slight compression, indicating a stabilizing market as major employers normalize post-AI hiring surges.
  • Midwest and Southeast roles cluster around $120–135K for Individual Contributors and $190–210K for Managers, offering high cost-adjusted value and growing regional opportunity.
  • Mountain region salaries have risen, narrowing the historical gap with coastal markets—especially among MG-2 roles tied to cloud infrastructure and data platform leadership.

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Gender Representation & Pay Equity in Data Engineering, 2025

Gender representation in data engineering is steadily moving toward greater parity, especially at the individual contributor level, where early-career hiring and visibility efforts are promoting balance across the field. Although full equity has not yet been reached at senior levels, 2025 data highlights promising signs that momentum for inclusive growth and advancement is gaining strength.

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Pay gaps remain modest (2–3%) across IC levels, indicating stronger pay parity among early- and mid-career engineers.

  • Female mean pay increased by 8–10% year-over-year, keeping pace with the overall market, supported by broader early-career representation and targeted advancement programs.
  • Continued investment in promotion velocity and leadership development for senior-level female data engineers remains the most effective way to close the remaining gap.
  • Burtch Works data currently shows 73.6% male and 26.4% female among Data Engineering Professionals by gender.

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Data Engineering Professionals
Distribution by Gender and Job Level
Comparison of Data Engineering Professionals
Mean Base Salaries by Gender

Experience & Tenure Trends in Data Engineering (2025)

Data engineering is still a relatively new field, with many mid-career professionals boosting the IC-2 level. This trend indicates that the discipline is still growing, drawing talent into foundational roles quickly, while senior experience remains limited. As the field develops, career paths, tenure standards, and leadership pipelines will become more crucial to monitor.

Key Take Aways:

  • Most professionals are in the mid-career (IC-2) level, showing that many in data engineering are still gaining experience rather than holding long-term senior positions.
  • Rapid upward mobility and portfolio diversification (cloud, AI/ML, data infrastructure) lead to flatter tenure distributions compared to more established engineering functions. 
  • Considering the function's relatively new status, continuous investment in structured career development and tenure-related benchmarks will be essential for future growth and retention.

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2025 Industry Breakdown: Data Engineering Compensation by Sector:

Industry continues to play a defining role in how Data Engineers are compensated, with salaries reflecting each sector’s stage of digital maturity, data dependency, and AI integration. In 2025, tech and telecom remain the clear leaders, while healthcare and financial services sustain strong upward momentum as data infrastructure becomes mission-critical. Across industries, mid-level engineers (IC-2) and senior managers (MG-3) see the greatest differentiation in pay tied to complexity and data scale.

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  • Tech/Telecom leads all sectors, with mean base pay exceeding $134K and medians near $228K reflecting continued investment in AI infrastructure and platform scalability.
  • Healthcare & Pharma ranks closely behind at ~$131K for IC-2 roles, underscoring data’s expanding role in patient analytics, clinical trials, and real-world evidence generation.
  • Financial Services and Consulting show steady IC-2 averages near $125–126K, which are consistent with demand for governance, risk modeling, and data compliance talent.
  • Advertising/Marketing and Retail maintain competitive base salaries (around $124–126K) driven by consumer data optimization and personalization initiatives.

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Overall, industry variation narrows slightly year over year, signaling a broader normalization of data engineering pay across sectors as skills become more transferable and cloud-based.

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Comparison of Data Engineering IC-2
Mean Base Salaries Across Industries

Base Salaries by Job Level and Industry for
Data Engineering Individual Contributors (ICs)

Base Salaries by Job Level and Industry for
Data Engineering Managers (MGs)

2025 Equity Insights

Job Seekers and Talent Acquisition Teams Taking Action


While our 2025 Compensation Report shows that pay gaps between men and women in Data Science and AI roles still exist, forward-thinking organizations see this challenge as an opportunity.

For hiring managers, addressing inequities isn’t just about fairness — it’s a competitive advantage. Teams that actively review and adjust compensation attract a broader pool of top talent, improve retention, and enhance employer brand reputation. By taking the lead on pay equity, you position your company as a top destination for innovators who value inclusion as much as impact.

For talent, especially women in AI and Data Science, today’s market offers a moment of leverage. As skills like Applied LLM Engineering, MLOps, and Retrieval-Augmented Generation grow in demand, professionals who demonstrate technical expertise and leadership in these areas are well-equipped to advocate for competitive pay and advancement opportunities.


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