Last Updated on September 28, 2026
A typical U.S. data engineer salary lands in the low-to-mid six figures. Most sources report average base salaries between $123,000 and $136,000, with total compensation ranging from $134,000 to $160,000 or higher depending on the employer.
The spread exists for a reason. Whether a source is reporting base salary or total compensation changes the number significantly. So does the mix of companies, locations, and seniority levels in the sample.
Data engineers build and maintain the pipelines that collect, transform, and deliver data for analysis. They design the infrastructure that makes analytics, reporting, and machine learning possible. In a market where every AI initiative depends on reliable data, this work sits at the center of how organizations operate and compete.
Indeed reports that working as a data engineer could bring in around $135,000 a year. That figure is useful as a starting point, but the real number depends on experience, location, company type, and the technical scope of the role.
What is the average data engineer salary?
There is no single correct number for the average data engineer salary. Different sources use different methodologies, sample different populations, and report different compensation components. The useful move is to look across several sources and understand what each one is actually measuring.
Most U.S. benchmarks cluster around $123,000 to $136,000 for average or base salary. Total compensation figures, which include bonuses, equity, and other cash, tend to run between $134,000 and $160,000 or more.
Here is how the major sources compare:
| Source | Reported figure | What it represents | Notes |
|---|---|---|---|
| Indeed | ~$135,000 | Average annual salary | Broad sample across industries |
| Salary.com | ~$123,053 | Average salary | Structured estimate |
| Glassdoor | ~$134,000 | Median total pay | Includes additional pay |
| Built In | ~$150,234 | Total compensation | Base plus additional cash comp |
| Levels.fyi | ~$160,000 | Median total compensation | Strong signal for tech-company comp |
| PayScale | ~$100,094 | Average base salary | Lower estimate, different sample mix |
The spread across these sources does not mean any of them are wrong. It means they are measuring different things. Salary depends on company size, location, experience level, and whether the figure includes equity or bonuses. Reading the label matters more than chasing a single number.
Why data engineer salary numbers vary so much
One of the most common frustrations when researching data engineering salary is seeing wildly different numbers across sites. One source says $100K. Another says $160K. Both can be accurate for the populations they are measuring.
The biggest driver of the gap is the distinction between base salary and total pay. Base salary is the fixed annual amount before bonuses or equity. Total compensation adds cash bonuses, stock grants, profit sharing, and sometimes signing bonuses. At large tech companies, equity alone can add $30,000 to $80,000 or more per year.
Other factors that shift the numbers:
- Self-reported vs. modeled data. Some sites rely on users submitting their salaries. Others model estimates from job postings and public filings.
- Metro-area skew. If a salary site has a disproportionate share of respondents from high-cost cities, the average rises.
- Company and seniority mix. A sample heavy on big tech seniors will look very different from one weighted toward mid-market generalists.
These salaries also do not always include compensation like cash bonuses or equity, so keep that in mind when comparing figures.
The practical recommendation: compare multiple sources and prioritize those that clearly label whether the figure is base or total compensation.
Data engineer salary based on experience
Experience is the clearest driver of data engineer salary. Entry-level data engineers are some of the best-paid engineers out there, and compensation grows meaningfully as scope and ownership increase.
This might be because data engineering is a popular field to transition into from another engineering profession. Or it could simply be that the skillset is in high demand. Either way, entry-level figures often reflect adjacent experience in software, analytics, or data work rather than a true zero-experience starting point.
Here is how data engineer salary by experience typically breaks down:
| Experience level | Typical range | Notes |
|---|---|---|
| Entry-level / 0 to 3 years | $80K to $105K | Some benchmarks show figures slightly lower at ~$77,381 |
| Mid-level / 4 to 6 years | $119K to $150K | Some sources report closer to ~$103,708 depending on sample |
| Senior / 7+ years | $147K to $179K+ | Aligns well with benchmarks around $156,625 |
| Staff / Principal | $175K to $220K+ | More common at larger or highly specialized companies |
What changes as experience grows
Entry-level roles focus on building and maintaining pipelines. The work centers on learning the stack, writing reliable transformations, and understanding data flow.
Mid-level engineers often own entire workflows. They take on data modeling, pipeline reliability, and cross-team coordination.
Senior roles shape architecture, performance tuning, cost optimization, and mentoring. The scope shifts from executing tasks to making design decisions that affect entire systems.
Staff and principal engineers influence platform strategy. They set technical direction across teams and often operate at the intersection of engineering and organizational decision-making.
It is also important to keep in mind that there are always opportunities for advancement and increased pay as you grow your data engineering skill set. Salary growth usually follows ownership, not just tool familiarity. The engineer who owns the reliability of a critical pipeline is worth more than one who has touched many tools but owned nothing end-to-end.
Data engineer salary based on location
Data engineer salary by location still matters, even in an era of hybrid and remote hiring. Salaries in tech hubs like San Francisco and Washington, DC, will be higher than in locations where there are fewer large tech employers or enterprise data teams.
Here is how reported salaries compare across major U.S. markets:
| Location | Reported salary | Interpretation |
|---|---|---|
| Washington, DC | $167,039 | Strong enterprise and government-adjacent demand |
| San Francisco | $152,457 | High-paying market with high cost of living |
| New York | $139,100 | Strong finance and enterprise demand |
| Boston | $110,684 | Solid market, lower than top-tier hubs in this data set |
San Francisco consistently leads across most salary sources, though Washington, DC, shows higher figures in some data sets due to the concentration of government contracting and enterprise consulting.
Remote roles introduced more geographic flexibility, but the landscape has shifted. Some companies localize pay based on where the employee lives. Others maintain broader pay bands. Fully remote roles are not as universally available as they seemed in 2021 and 2022, and hybrid arrangements are now the more common pattern.
If you are evaluating a remote data engineer salary, check whether the employer adjusts compensation by location. That single policy can swing an offer by $15,000 to $30,000 or more.
Data engineer salary based on company size and industry
Company size creates a clear gradient in data engineering salary. Larger organizations generally pay more, and the pattern holds across most data sources.
| Company size | Average salary |
|---|---|
| 0 to 50 employees | $99,903 |
| 51 to 500 employees | $107,283 |
| 501 to 1,000 employees | $109,170 |
| 5,000+ employees | $117,939 |
These figures do not include compensation like cash bonuses or equity. At larger firms, especially in big tech, stock-based compensation can materially increase total pay. Larger companies also tend to have clearer leveling systems, bigger infrastructure budgets, and more defined career ladders.
Smaller companies may pay less in base salary but can offer broader ownership. You might own the entire data platform at a 40-person startup, which builds experience faster than maintaining one pipeline in a 500-person data org.
Industry also shapes pay in ways that are easy to overlook:
| Factor | Salary pattern | Why it matters |
|---|---|---|
| Big tech | Highest total comp | Equity and bonus can materially increase pay |
| Finance | Often high salaries | Real-time, regulated, business-critical data |
| Energy | Often overlooked high payer | Large-scale operational and sensor data |
| Healthcare | Premium in some markets | Compliance and sensitive data add complexity |
Do not judge a role on salary alone if the stack is narrow or legacy-heavy. A slightly lower-paying role with strong cloud and streaming exposure can be a better long-term move than a higher-paying role that limits your growth to a single legacy system.
Which skills raise a data engineer’s salary?
Not all data engineer skills carry the same weight in compensation discussions. Some are baseline expectations. Others command a premium because they enable work on more complex, higher-value systems.
Baseline skills that most roles require:
- SQL
- Python
- ETL design and implementation
- Data modeling
- Data warehouses
- Cloud fundamentals
These get you hired. They do not, by themselves, push you into the top salary brackets.
Salary-premium skills that tend to raise offers:
| Skill | Where it shows up in the job | Why it can raise pay | Tradeoff or limitation |
|---|---|---|---|
| SQL | Querying, transformation, warehousing | Core to almost every data engineering role | Not enough by itself for top-end roles |
| Python | ETL, orchestration, automation | Widely used across platforms | Needs production-quality practices |
| Spark | Large-scale batch processing | Valuable for high-volume systems | Overkill for smaller workloads |
| Snowflake | Cloud warehousing | Strong demand in modern data teams | Can become platform-specific |
| Kafka | Event streaming | Valuable in real-time systems | Higher operational complexity |
| Databricks | Lakehouse workflows | Strong enterprise demand | Most valuable in data-mature teams |
| AWS / GCP / Azure | Cloud infrastructure and security | Broadens access to modern roles | Depth in one cloud often beats shallow coverage of all three |
The skills that usually move pay higher
SQL and Python are the starting point, not the ceiling. Higher pay typically follows when an engineer owns responsibility for scalability, performance, reliability, observability, and cloud cost optimization.
Tools matter more when paired with production responsibility. Knowing Spark is useful. Operating a Spark-based pipeline that processes billions of records daily and being accountable for its uptime is what moves compensation.
Project evidence matters more than course completion alone. If you are building data engineering skills, Udacity’s Programming for Data Science with Python program is a practical starting point that focuses on building with SQL and Python in applied contexts.
Data engineer vs data scientist salary
Data engineers and data scientists are not the same role, though the titles get confused often enough to cause real uncertainty about compensation.
Data engineers build and maintain data pipelines and infrastructure. They focus on making data available, reliable, and performant. Data scientists analyze data and build models. They focus on extracting insights, running experiments, and developing machine learning systems.
In terms of salary, the two roles overlap more than most people expect, especially at mid and senior levels. Both can reach well into six figures. Data engineering can pay particularly well because production data systems are critical to analytics and machine learning. Without reliable pipelines, models do not get the data they need.
The better question is not which pays more. It is which kind of work fits how you think. If you prefer systems, infrastructure, and backend problem-solving, data engineering is the stronger fit. If you are drawn to experimentation, statistics, and modeling, the Data Scientist Nanodegree path may align better.
Job outlook for data engineers
The Bureau of Labor Statistics forecasts a 22% increase in job growth from 2020 to 2030 within the broader field of data occupations. That rate is much higher than average job growth across the economy. The BLS figure applies to a broader data occupation grouping, but it serves as useful directional context for data engineering specifically.
The demand signal behind these numbers is straightforward. Organizations are migrating to the cloud, modernizing data platforms, and building production AI systems that depend on reliable data infrastructure. Every machine learning model, dashboard, and automated decision system needs clean, well-structured data flowing through well-maintained pipelines.
AI systems are only as useful as the data infrastructure behind them. That makes data engineering one of the skills that matter most in the AI economy. As companies move from experiment to production with their AI investments, the need for engineers who can build and operate data systems at scale is growing, not shrinking.
How to increase earning potential as a data engineer
Salary data is useful for benchmarking. What actually moves your compensation over time is the combination of skills you build and the work you can demonstrate.
Here is what tends to have the most impact:
- Build strong SQL and Python fundamentals. These are the foundation for everything else. Weak fundamentals create a ceiling.
- Learn one cloud platform deeply. AWS, GCP, or Azure. Depth in one is more valuable than surface-level familiarity with all three.
- Get hands-on with Spark, orchestration tools, warehouses, or streaming systems. These are the technologies that separate entry-level from mid-level and above.
- Build portfolio projects that show ETL design, warehouse modeling, batch workflows, streaming workflows, and cloud deployment. The Data Engineer Nanodegree is built around exactly this kind of project-based work.
- Target roles with production exposure. Operating systems in production builds skills that are hard to replicate in coursework alone.
- Learn to discuss reliability, cost, scalability, and monitoring in interviews. These are the concerns that senior engineers care about, and interviewers notice when you can speak to them.
- Compare offers using total compensation, not just base salary. Equity, bonuses, and benefits can shift the real value of an offer by tens of thousands of dollars.
- Consider certifications that align with your goals. AWS and cloud-specific certifications can signal readiness for roles at companies that use those platforms.
Employers pay more for demonstrated pipeline and platform capability than for course completion alone. The most effective approach combines structured learning with building things you can show and explain.
Is data engineering a good career if salary is a priority?
Yes, with context. Data engineering remains one of the stronger-paying paths in data and backend engineering. Salaries start high relative to many other technical roles and scale well with experience and scope.
The role is the best fit for people who enjoy systems, infrastructure, databases, and backend problem-solving. If you like designing how data moves through an organization and making sure it arrives reliably, the work itself will sustain you beyond the paycheck.
It is a less natural fit for people primarily interested in experimentation, research, or statistical modeling. Those interests align more closely with data science or machine learning engineering roles.
The compensation floor is strong. The ceiling depends on the complexity of the systems you work on, the scale of the data you manage, and your ability to own outcomes rather than just execute tasks.
Conclusion
Many data engineer salaries land in the low-to-mid six figures for base pay. Total compensation can run higher at larger employers or companies that offer meaningful equity. Experience, location, company type, and technical scope explain most of the variation across salary sources.
The factors outside your control (where companies are headquartered, how they structure equity) will always shape part of the picture. The factor you can control is what you build and what you can demonstrate. Skills that matter in the AI economy are the most reliable lever for increasing your earning potential over time.
If you are ready to start building those skills, consider joining our specialized courses. Udacity’s programs focus on applied, project-based learning in data engineering, cloud platforms, and the tools that modern data teams actually use. Explore Udacity’s School of Data Science to find the path that fits where you are now and where you want to go.
