Data Engineer Salary Guide

Data engineers earned a median annual wage of $112,590 in May 2024, based on the BLS Data Scientists classification (SOC 15-2051) that encompasses data engineering roles [1]. With employment projected to grow 34 percent from 2024 to 2034, data engineering stands as one of the fastest-expanding disciplines in technology [2].

Key Takeaways

• The national median salary for data engineers is $112,590 per year ($54.13 per hour) under the BLS Data Scientists classification [1]
• Earnings range from $63,650 at the 10th percentile to $194,410 at the 90th percentile [3]
• Projected employment growth of 34 percent through 2034 far exceeds the national average [2]
• Approximately 245,900 data scientist and data engineer positions exist nationally [2]
• Real-time streaming and ML pipeline engineering command the highest specialization premiums

National Salary Overview

Data engineers are classified by the BLS primarily under Data Scientists (SOC 15-2051), though some roles overlap with Software Developers (SOC 15-1252) depending on the specific position [1][4]. The SOC 15-2051 classification best captures the data infrastructure, pipeline, and platform work central to data engineering. The May 2024 wage distribution is as follows [1][3]:

The $130,760 spread between the 10th and 90th percentiles reflects the breadth of the data engineering discipline [9]. Entry-level data engineers building basic ETL pipelines operate on a fundamentally different compensation scale than senior engineers designing real-time streaming architectures processing billions of events daily.

The median of $112,590 exceeds the national median for all occupations ($49,500) by 127 percent [6]. The interquartile range of $82,630 to $155,810 captures the compensation band where most working data engineers fall, from those with a few years of experience to established senior professionals [3].

Salary by Experience Level

Data engineering compensation scales with both years of experience and the sophistication of systems a professional can design and maintain [2][7].

Entry-Level Data Engineer (0-2 years): New data engineers, often transitioning from software development or analytics roles, typically earn between $63,650 and $82,630, the 10th to 25th percentile range [3]. Proficiency in SQL, Python, and basic ETL tooling (Airflow, dbt) defines this level.

Mid-Level Data Engineer (3-5 years): Engineers with production experience building and maintaining data pipelines, data warehouses, and data lakes earn between $82,630 and $112,590, the 25th to 50th percentile [3]. Expertise with cloud data services (Snowflake, BigQuery, Redshift, Databricks) and orchestration platforms differentiates this tier.

Senior Data Engineer (6-10 years): Senior professionals designing enterprise data platforms, real-time streaming systems, and complex data mesh architectures earn between $112,590 and $155,810, the 50th to 75th percentile [3]. These engineers often serve as technical leads and make critical technology selection decisions.

Staff/Principal Data Engineer (10+ years): Staff and principal data engineers who define data strategy at the organizational level earn $155,810 to $194,410 and above [3]. At major technology companies, total compensation for staff-level data engineers (including equity) can exceed $350,000.

Top-Paying States

Data engineering compensation concentrates in states with significant technology and financial services sectors [5][8].

Washington leads at $149,800, driven by the concentrated presence of Amazon, Microsoft, and other data-intensive technology companies in the Seattle-Tacoma corridor [5]. California follows closely at $147,200, reflecting Silicon Valley's insatiable appetite for data infrastructure professionals [8].

Top-Paying Metro Areas

Metropolitan areas housing major technology employers and financial institutions offer the highest data engineering compensation [5].

San Jose leads at $165,800, reflecting demand from both technology giants and the startups that comprise the data infrastructure ecosystem [5]. The rise of remote work has enabled data engineers in lower-cost metros to command salaries closer to hub-level compensation at companies with location-agnostic pay policies.

Salary by Specialization

Data engineering specializations carry distinct compensation profiles [2][7].

Real-Time Streaming Engineering (Kafka, Flink, Spark Streaming): Engineers building low-latency streaming pipelines for real-time analytics and event-driven architectures command premiums of 15-25 percent above generalist data engineers, reflecting the complexity and business criticality of these systems.

ML/AI Platform Engineering: Data engineers who build and maintain machine learning infrastructure (feature stores, model serving, experiment tracking) occupy the intersection of data engineering and ML engineering, earning at the 75th percentile and above ($155,810+) [3].

Cloud Data Platform Architecture (Snowflake, Databricks, BigQuery): Specialists in cloud-native data platforms earn near the 50th-75th percentile, with certified Snowflake or Databricks engineers commanding additional premiums.

Data Governance and Quality Engineering: Engineers focused on data catalog management, lineage tracking, and quality frameworks earn near the median, with premiums in regulated industries requiring compliance expertise.

Analytics Engineering (dbt, LookML): The emerging analytics engineering discipline, bridging data engineering and analytics, typically earns at the 25th-50th percentile ($82,630-$112,590), though senior analytics engineers at data-mature organizations can exceed the median [3].

Benefits and Total Compensation

Data engineering roles at technology companies typically come with substantial additional compensation [7].

Equity Compensation: RSU grants at publicly traded technology companies add 20-60 percent to base salary for data engineers. At companies like Netflix, Meta, and Databricks, equity can equal or exceed base salary at senior levels.

Annual Bonuses: Performance bonuses of 10-20 percent of base salary are standard, with higher percentages at financial institutions and hedge funds that rely heavily on data infrastructure.

Learning and Conference Budgets: Data engineering conferences (Data Council, dbt Coalesce, Kafka Summit) and platform certification courses ($2,000-$8,000 annually) are routinely employer-funded.

Remote Work: Data engineering is among the most remote-friendly disciplines, with Dice reporting that over 65 percent of data engineering job postings offered remote or hybrid arrangements as of 2024.

Health and Retirement: Standard technology company benefits (comprehensive health insurance, 401(k) matching of 4-6 percent, HSA contributions) add $20,000-$35,000 in annual value.

How to Negotiate Your Data Engineer Salary

The 34 percent projected growth rate and persistent talent shortage give data engineers substantial negotiating power [2].

1. Benchmark against BLS percentile data. The 25th-75th percentile range ($82,630-$155,810) provides objective market context [3][10]. Position yourself within this range based on years of experience, platform expertise, and scale of systems managed.

2. Quantify data pipeline scale and reliability. Processing volume (TB/PB daily), pipeline uptime (99.9 percent+), and data freshness SLAs translate directly to business value. These metrics should anchor your negotiation narrative.

3. Leverage cloud platform certifications. Snowflake SnowPro Core/Advanced, Databricks Data Engineer Associate/Professional, and AWS Data Analytics Specialty certifications each justify 5-10 percent salary increases.

4. Highlight cost optimization impact. If your architecture redesign reduced cloud data processing costs by 40 percent or eliminated $500,000 in annual vendor spend, those savings directly justify premium compensation.

5. Negotiate for data tooling budget. Personal development environments and experimental cluster access ($1,000-$5,000 annually) are low-cost for employers but high-value for career growth.

6. Consider the startup equity trade-off. Early-stage data infrastructure companies (data observability, data quality, vector databases) offer equity packages that can be worth multiples of salary premiums if the company succeeds.

7. Use the growth projection as context. With 34 percent employment growth projected through 2034 [2], employers face a structural talent shortage that makes retention-competitive compensation a business necessity.

Salary Growth and Career Progression

Data engineering career trajectories show strong salary growth over time [2][7].

From entry-level ($63,650-$82,630) to senior data engineer ($112,590-$155,810) typically spans 5-7 years and represents a 75-90 percent salary increase [3]. The progression is accelerated for engineers who develop specializations in streaming, ML platforms, or cloud-native data architectures.

Beyond the individual contributor track, data engineers advance into Head of Data Engineering ($160,000-$220,000), VP of Data ($200,000-$300,000), and Chief Data Officer ($250,000-$450,000+) roles [7]. The CDO path requires both deep technical credibility and business strategy capabilities.

With 34 percent projected employment growth from 2024 to 2034 and approximately 21,500 annual openings [2], demand for data engineers is expected to remain strong. The proliferation of AI/ML workloads, real-time analytics requirements, and data governance regulations continues to expand the scope of data engineering work.

Key Takeaways and Next Steps

Data engineers earn a median of $112,590 nationally, with the top 10 percent exceeding $194,410 [1][3]. The 34 percent projected growth rate makes this one of the fastest-growing technical professions [2]. Cloud platform expertise, streaming specialization, and ML infrastructure experience each provide levers for maximizing compensation.

To position yourself competitively in the data engineering job market, your resume needs to convey both technical depth and business impact. ResumeGeni's AI-powered resume builder helps data engineers articulate their pipeline architectures, scale achievements, and platform expertise in a format optimized for both human reviewers and ATS screening.

Frequently Asked Questions

What is the average salary for a data engineer in 2025? The national median salary is $112,590 per year ($54.13 per hour) based on BLS May 2024 data for the Data Scientists classification [1]. Industry-specific surveys often report slightly higher figures for dedicated data engineering roles.

How much do entry-level data engineers make? Entry-level data engineers typically earn between $63,650 and $82,630, corresponding to the 10th through 25th percentile [3]. Those with computer science degrees and internship experience at data-intensive companies start near the upper end.

Which state pays data engineers the most? Washington leads at approximately $149,800 in annual mean wages, followed by California at $147,200 [5][8]. Both states host the headquarters of major data platform companies (Snowflake, Databricks, Amazon).

Is data engineering a good career financially? Data engineering offers exceptional financial returns. The median of $112,590 exceeds the national median ($49,500) by 127 percent, and 34 percent projected job growth through 2034 ensures sustained demand [1][2][6]. The profession also offers clear paths to senior technical and leadership roles exceeding $200,000.

Do data engineers earn more than data analysts? Significantly. BLS data shows data scientists (which includes data engineers) earn a median of $112,590, while data analysts (often classified under SOC 15-2041, Statisticians, or SOC 13-1111, Management Analysts) typically earn $60,000-$85,000 [1]. The engineering complexity of building and maintaining data infrastructure commands a substantial premium over analysis work.

What tools and skills increase a data engineer's salary? Cloud data platforms (Snowflake, Databricks, BigQuery), streaming technologies (Kafka, Flink), orchestration tools (Airflow, Dagster), and infrastructure-as-code (Terraform) each correlate with salary premiums [7]. Python and SQL remain foundational, but Spark, dbt, and Kubernetes expertise differentiate higher-earning professionals.