Data Engineer Salary Guide: Entry-Level to Senior Pay in 2026

Salary is the most-searched topic in data engineering. And for good reason — there's a massive spread between what the lowest-paid and highest-paid data engineers earn. The difference between a $95K offer and a $180K offer often comes down to a few variables that most candidates don't think about: industry, location, total comp structure, and negotiation.

This guide cuts through the noise with verified data from four independent sources: the Bureau of Labor Statistics, Glassdoor, Salary.com, and Built In. No synthetic numbers, no "estimated" ranges — every figure links back to a specific, auditable source.

Data Engineer Salary Overview 2026

There's no single "data engineer salary." Different sources measure different things — base salary vs. total compensation, specific job title vs. broader occupational category, self-reported vs. employer-verified. Understanding these differences is essential for benchmarking your own pay.

Multi-Source Salary Model

Why four sources? Because no single source captures the full picture. Each has different methodology, sample size, and compensation definitions.

Salary by Experience Level

Experience is the single biggest salary lever. The jump from entry-level to senior is substantial — roughly 45-50% — and it compounds further at staff and principal levels.

Detailed breakdown by level:

What changes at each level:

• Entry-level ($97K): Execute tasks, learn the stack, build one pipeline end-to-end. Proving reliability matters more than speed.
• Mid-level ($120K–$134K): Own systems independently. Design data models, handle on-call, mentor juniors. This is where salary jumps fastest.
• Senior ($143K–$147K): Architect systems, make technology decisions, lead cross-team projects. The premium reflects decision-making responsibility.
• Staff/Principal ($160K–$195K+): Set technical direction for the organization. At this level, base salary is often just 50-60% of total compensation — equity becomes the dominant component.

Salary by Location

Location creates the widest salary gaps in data engineering. A data engineer in San Francisco earns ~$35K more than one in a mid-market city — but cost of living often absorbs the difference.

Why location matters less than it used to:

Since 2020, the shift to remote work has compressed geographic salary differences for data engineers. Many companies now use national pay bands or tiered location systems (Tier 1: SF/NYC/Seattle, Tier 2: Austin/Denver/Boston, Tier 3: everywhere else) with 10-15% gaps between tiers instead of the 30-40% gaps that existed pre-pandemic.

Salary by Industry

The same data engineering role pays differently depending on where you work. The BLS OEWS breaks this down by industry for the Data Scientists category:

What this means in practice:

• Tech companies and consultancies (computer systems design) pay the most — this includes FAANG, enterprise software, and systems integrators
• Corporate data engineering (management of companies, insurance) pays competitively but with lower upside — more stability, less equity
• The industry gap is ~$20K between the highest and lowest BLS categories — that's a real difference in annual income for the same skill set

Total Compensation Breakdown

Base salary is only part of the story. For mid-level and senior data engineers, total compensation includes three components:

How total comp stacks up across sources:

• Glassdoor: $131,152 median total pay ($103,699 base + $27,453 bonus/equity)
• Salary.com: $139,433 average total cash ($133,536 base + $5,897 bonus)
• Built In: $150,372 average total comp ($125,976 base + $24,396 additional cash)

The variation comes from equity. At startups and large tech companies, RSUs (restricted stock units) can add $30K–$100K+ annually to total compensation. At mid-market companies, bonus is typically 5-15% of base with minimal equity. At non-tech companies, the package is usually base + modest bonus.

Data Engineer vs Related Roles

How does data engineering compensation compare to adjacent roles? This context helps if you're choosing between paths or considering a transition.

Key comparisons:

• Data Engineer vs Software Engineer: Software developers have a higher BLS median ($131,450 vs $112,590) because the BLS software developer category is narrower and more precisely defined. In practice, platform sources show data engineers and software engineers earn comparably — the Glassdoor median for data engineers ($131K) is nearly identical to the BLS software developer median.
• Data Engineer vs Data Analyst: Data engineers earn significantly more. The closest BLS proxy for data analysts (Market Research Analysts) shows a $76,950 median — roughly $36K less. Glassdoor shows $131K vs $108K for the specific titles.
• Data Engineer vs Data Scientist: In the BLS, they're the same category. In practice, compensation is similar, with data scientists having slightly higher base salaries at some companies but data engineers having more consistent demand.

What Affects Data Engineer Pay

Beyond experience and location, several factors create salary variation within the same job title.

Cloud Platform Expertise

AWS, Azure, and GCP certifications and hands-on experience command premiums. Companies migrating to the cloud pay more for engineers who can design cloud-native architectures from scratch.

Streaming and Real-Time Skills

Engineers who work with Kafka, Flink, or Spark Streaming tend to earn more than those focused on batch-only ETL. Real-time data infrastructure is harder to build and maintain, and the talent pool is smaller.

Data Modeling and Architecture

Senior roles that involve dimensional modeling, schema design, and data mesh/data fabric architecture are compensated at the top of the range. This is the skill that separates a $130K engineer from a $170K architect.

Programming Depth

Production-grade Python, Scala (for Spark), and strong SQL optimization skills drive higher compensation. The more languages and paradigms you can work in at production quality, the more you're worth.

Certifications

Cloud certifications (AWS Data Engineer, Azure Data Engineer, Databricks) provide the clearest verifiable signal for hiring managers. While the direct salary premium is hard to isolate, certified engineers tend to land roles at higher-paying companies.

How to Negotiate a Higher Salary

Data is your best negotiation tool — and as a data engineer, you should be better at using it than most.

1. Know Your Market Number

Use the multi-source data in this guide to establish your target. Cross-reference:

• BLS OOH for the government baseline ($112,590 median for the category)
• Glassdoor for title-specific total pay ($131,152 median)
• Salary.com for percentile ranges ($120K 25th, $134K 50th, $147K 75th)
• Built In for city-specific data (adjust for your market)

2. Anchor on Total Compensation

Don't negotiate base salary alone. Ask about:

• Sign-on bonus — one-time cash to bridge the gap between current and new comp
• RSU/equity grants — especially at public companies where the value is liquid
• Annual bonus target — what percentage, and is it guaranteed for year one?
• Review timeline — can you get an accelerated 6-month review instead of 12?

3. Leverage Competing Offers

The strongest negotiating position is having a competing offer. Even if you prefer Company A, an offer from Company B gives you concrete leverage. Data engineering talent is in high demand — if you have the skills, getting multiple offers is realistic.

4. Quantify Your Impact

When negotiating a raise (not a new hire), come with data:

• Pipeline uptime improvements
• Query performance optimizations (from X seconds to Y)
• Cost savings from cloud infrastructure changes
• Data quality improvements that prevented downstream issues

Prepared by

Careery Team

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Reviewed by

Bogdan Serebryakov

Researching Job Market & Building AI Tools for careerists since December 2020