Data Engineer
$113,333
avg. annual salary
Site Reliability Engineer
pays more on average
Site Reliability Engineer
$120,337
avg. annual salary
When considering a career in tech, understanding salary expectations is crucial. Data Engineers and Site Reliability Engineers (SREs) are both integral to modern IT landscapes, yet their roles, responsibilities, and compensation packages can vary significantly. Data Engineers primarily focus on designing and maintaining data pipelines, ensuring that data is accessible and usable for analysis. On the other hand, Site Reliability Engineers work to ensure the reliability and uptime of systems and applications. This comparison will delve into the average salaries, potential growth, benefits, and career trajectories for both professions, providing you with the insights necessary to make an informed decision about your future in technology.
Salary by Experience Level
starting salary
starting salary
avg. difference (6%)
Average Salaries
As of January 2025, the average salary for a Data Engineer is $112,000, with a typical range of $95,000 to $130,000. In contrast, Site Reliability Engineers earn an average of $120,000, with salaries ranging from $100,000 to $140,000.
Factors such as location, experience level, and company size can influence these figures, so understanding the broader market trends is essential.
Salary by Experience Level
Both career paths offer competitive salaries that increase with experience. For Data Engineers, entry-level positions start around $80,000, while mid-level roles average $110,000 and senior positions can reach upwards of $150,000.
For SREs, entry-level salaries begin at approximately $85,000, with mid-level positions averaging $120,000 and senior ones often exceeding $160,000.
Benefits and Compensation Packages
In addition to base salaries, both Data Engineers and Site Reliability Engineers often receive bonuses, stock options, and comprehensive benefits packages. Typical perks include health insurance, retirement plans, paid time off, and professional development opportunities.
While both roles receive similar benefits, SREs may have a slight edge in bonuses due to their critical impact on system performance.
Career Paths and Growth Opportunities
Data Engineers can progress to roles such as Data Architect or Machine Learning Engineer, focusing on more complex data systems and analytical methodologies. Site Reliability Engineers can advance to become Engineering Managers or DevOps Leaders, responsible for larger teams and broader system architectures.
Both paths offer lucrative growth opportunities, with salaries reflecting increased responsibility.
Factors Influencing Salary
Several factors can affect the salaries of Data Engineers and SREs, including location, company demand, industry standards, and individual skill sets. Major tech hubs like San Francisco and New York often offer higher salaries to attract talent.
Additionally, certifications in relevant technologies can also lead to salary increases.
Detailed Salary Comparison: Data Engineer vs Site Reliability Engineer
Below is a focused salary breakdown that highlights real differences and career milestones.
- •Typical U.S. salary ranges:
- •Data Engineer: $85,000–$170,000 annually
- •Site Reliability Engineer (SRE): $95,000–$190,000 annually
- •Median pay: SREs earn about 10%–20% more than Data Engineers at mid-career levels (example: $135k vs $120k).
- •Entry-level (0–2 years): Data Engineers typically start at $75k–$95k; SREs at $90k–$110k.
- •Senior/lead (5+ years): Data Engineers $150k–$200k; SREs $160k–$240k, often boosted by on-call premiums and equity.
- •Total compensation: bonuses and stock commonly add 15%–40% to base salary.
Actionable takeaway: If you value higher base pay and stability, aim for SRE roles; if you prefer analytics and ETL ownership, target Data Engineer paths.
Key Factors That Affect Salaries and How to Influence Them
Consider these concrete factors when comparing offers and planning career moves.
- •Location: Salaries in Bay Area, NYC, and Seattle run 20%–40% above national averages. Remote roles often pay 5%–15% less than local high-cost markets.
- •Skill set: Cloud + container skills (AWS/GCP + Kubernetes) can boost pay by 8%–12%. Advanced data skills (Spark, data modeling) add 7%–10%.
- •Experience and scope: Managing teams or owning production services typically increases pay by 15%–30%.
- •On-call and hours: Regular on-call duty can raise effective compensation by $2,000–$8,000 annually or 5%–10% of salary.
- •Industry: Finance and ad-tech tend to pay 10%–25% more than education or non-profit.
Actionable takeaway: Prioritize one high-impact skill (e. g.
, Kubernetes for SRE or Spark for data) and seek roles in higher-paying industries or locations to maximize salary growth.