The average data engineer salary in the United States sits between $125,000 and $135,000 in base pay as of early 2026, depending on the source. Entry-level roles start around $80K to $105K. Mid-level engineers with four to six years pull $119,000 to $150,000. Seniors with seven or more years regularly clear $147K to $179K+ in base alone, and total comp at large tech companies blows past $200,000. This guide breaks down data engineer compensation by experience, city, industry, and skill set using data from Glassdoor, ZipRecruiter, Indeed, PayScale, Built In, and the Motion Recruitment 2026 Tech Salary Guide.
We place data engineers at KORE1 pretty much every week. The salary conversation is always first and it always goes the same way. Hiring manager tells us their budget. We tell them the market moved. Pause. Then we figure it out together. If your numbers are more than six months old, they’re probably wrong. This guide exists so you can skip that awkward opener.
What Data Engineers Earn in 2026, by Experience Level
A data engineer salary is the total compensation paid to professionals who design, build, and maintain the data pipelines and infrastructure that power analytics, reporting, and AI systems. In 2026, these salaries vary wildly by experience level, and mid-level candidates are far and away the hardest to hire.
I pulled from six sources to build the table below because no single aggregator gets it right. Glassdoor uses self-reported data and wraps in additional comp estimates, which inflates things. PayScale skews early-career because of who fills out their surveys. ZipRecruiter reflects what’s being posted, not what’s being accepted. Indeed averages from job ads going back 36 months, so some of those numbers are fossil data from 2023 that’s still dragging down the average and making things look cheaper than they actually are in 2026. You get the idea. You need the composite.
| Experience Level | Base Salary Range | What We’re Seeing in Placements |
|---|---|---|
| Entry-Level (0-3 years) | $80,000 – $105,000 | Strong SQL and Python. Cloud exposure helps but isn’t required yet. |
| Mid-Level (4-6 years) | $119,000 – $150,000 | The bloodbath bracket. Everyone wants them. Nobody has enough. |
| Senior (7+ years) | $147,000 – $179,000+ | Architects and leads blow past $180K. Big tech total comp often north of $250K. |
| Staff / Principal | $175,000 – $220,000+ | Unicorns. They don’t apply to jobs. You go find them or you don’t get them. |
Glassdoor, 32,349 salaries, March 2026. ZipRecruiter, March 2026. PayScale, 4,384 profiles, Jan 2026. Built In. Motion Recruitment 2026 Tech Salary Guide.
Those are base numbers only. At Meta and Google and Apple and Amazon and the well-funded Series C startups, total comp with equity and bonuses and RSUs changes the picture completely. A senior data engineer at Google? $250K to $350K all-in. I know. It sounds made up. It’s not. We lose candidates to packages like that all the time and there is genuinely nothing a 200-person company in Phoenix can do to match it, which is frustrating but also kind of the point, you’re not supposed to try to match it. You’re supposed to compete differently. I’ll come back to that.
Mid-level is the war zone.
Four to six years, solid pipeline work, comfortable with at least one major cloud platform, ideally some Spark or Snowflake exposure. These people get three or four offers within a week. Not a month. A week. So if your interview process involves six rounds spread across eight weeks with a take-home assignment and a culture fit panel and two separate technical screens and then a compensation committee meeting before you can extend an offer, they will be gone before you get to round four, and you’ll be back on LinkedIn wondering why nobody good is applying when in reality plenty of good people were available, your process just took so long that they all accepted other offers while you were still scheduling the next interview, and I realize that’s a run-on sentence but it needed to be because that’s exactly what the experience feels like from our side of the table. Exhausting.
Data Engineer Salary by City
Geography used to be the whole ballgame. Five years ago I would have said 80% of the salary story was where the job was located. Now it’s maybe half. Remote and hybrid rearranged the math. But location still moves the number, especially in metros where cost of living forces pay up whether anyone wants it to or not.
San Francisco still tops the list. Obviously.
Scroll down to Houston though.
| City | Median Total Pay | Notes |
|---|---|---|
| San Francisco, CA | $180K – $220K | Still the ceiling. Mid-level alone runs $148K-$186K here. |
| San Jose, CA | $172,000 | Apple, Cisco, the whole South Bay corridor. |
| Houston, TX | $173,000 | Yeah. Houston. Energy data infrastructure is paying real money. |
| New York, NY | $144,000 | Finance drives it. Banks need pipeline talent badly. |
| Seattle, WA | $146K | Amazon effect plus Microsoft. No state income tax is the hidden bonus. |
| Los Angeles, CA | $140,000 | Entertainment analytics and media personalization. |
| Austin, TX | $129K | Growing fast. Oracle, Tesla, and a mountain of startups moved here. |
| Boston, MA | $129,000 | Healthcare and biotech data engineering. Pays quietly well. |
| Denver, CO | $126K | Lower cost of living with close-to-national comp. |
| Chicago, IL | $129,000 | Financial services and trading firms. |
| Remote (U.S.) | $122K – $153K | Some companies adjust for location. Some don’t. Ask first. |
Glassdoor (October 2025 and March 2026 data). Coursera analysis of Glassdoor data. Motion Recruitment 2026 Tech Salary Guide. ZipRecruiter.
Houston at $173K. The city nobody puts on their tech salary list just showed up and beat New York, Seattle, and basically everywhere except the two Bay Area metros. Think about it though. Exxon, Chevron, Shell, and a dozen other energy companies are all building out massive data platforms right now for AI and predictive maintenance and exploration analytics and regulatory reporting. They need Spark engineers and Snowflake admins and Kafka architects the same as any startup in the Bay Area. And they’re competing for the same people. So they have to pay Bay Area prices. But without the $3,500 studio apartment. The math writes itself.
And look, if you’re a mid-market company in Denver benchmarking against San Francisco Big Tech, stop. You’re going to make yourself crazy for no reason. Your comp doesn’t need to match Google’s. It needs to be competitive within your actual hiring market, against the other companies your candidates are actually interviewing with. That’s a very different exercise. Our salary assistant tool can help with that.
Skills That Push Pay Higher
Python and SQL are oxygen at this point. According to 365 Data Science’s analysis of 943 real job listings, 70% require Python and 69% require SQL. Everyone in the candidate pool has both. So having them doesn’t get you paid more. It just gets you in the door.
The premium skills, the ones that separate a $120K offer from a $160K offer, are more specific.
- Apache Spark and distributed computing. Still the dominant big data framework, showing up in about 39% of postings. If you can write optimized Spark jobs and actually understand how distributed processing works under the hood rather than just having followed a Udemy course once, you’re in a completely different candidate pool than someone who only knows pandas. And companies know the difference fast. Usually by the second technical interview.
- Snowflake. About 29% of postings now, up massively from two years ago. Cloud-native warehousing. The fastest path to a salary bump if you also know dbt and Airflow.
- Databricks and lakehouse architecture. Around 17% of postings. Newer, which means supply is tight, which means the companies adopting it tend to be well-funded enough to overpay rather than wait for the market to catch up. I’d bet this number doubles within eighteen months based on the adoption curve we’re seeing in enterprise, but I’ve been wrong about timing before so take that with whatever amount of salt you think is appropriate.
- Real-time streaming, meaning Kafka, Flink, Kinesis. Totally different salary tier than batch-only engineers. Full stop. Kafka alone shows up in roughly a quarter of postings. If you can build and maintain production streaming pipelines, you can basically name your price in this market. I realize that sounds like recruiter hype. It’s genuinely not. The supply-demand imbalance for streaming-experienced data engineers is worse than almost any other sub-specialty we recruit for, and we recruit for a lot of sub-specialties.
- Deep cloud platform knowledge. Not “I’ve used S3 a few times.” Certification-level depth where you can architect a solution from scratch and explain the cost tradeoffs. AWS leads demand. Azure owns enterprise. Real depth in at least one adds roughly $10K to $20K to offers in our experience. That’s from placement negotiations, not a published number, so grain of salt. But it keeps showing up.
That same 365 Data Science analysis found an almost perfect 50/50 split between companies hiring specialists versus generalists. Half want someone who goes absurdly deep on streaming or cloud architecture. Other half want a Swiss Army knife. Know which one you’re walking into before you negotiate.
Data Engineer vs. Data Scientist Salary
Everyone asks this. Every. Single. Time.
| Role | Median Base (National) | Growth Outlook |
|---|---|---|
| Data Engineer | $125K – $135K | ~23% YoY (industry estimate, 365 Data Science and others) |
| Data Scientist | $112,590 (BLS median, 2024) | 33.5% projected 2024-2034 (U.S. Bureau of Labor Statistics) |
| Big Data Engineer | $142K | Higher specialization premium within the same field |
U.S. Bureau of Labor Statistics Occupational Outlook Handbook, 2024-2034. Glassdoor. Coursera/Glassdoor analysis.
Data engineers out-earn data scientists on base salary. In most comparisons. Which surprises a lot of people because the popular narrative is that data science is the sexier, higher-paying role. The BLS puts the median data scientist wage at $112,590 for 2024. Most data engineer aggregators land between $125K and $135K. I’m not saying that because I’m a recruiter who benefits from higher engineering salaries. The numbers just come out this way across every source I checked.
But total comp flips it sometimes. At Big Tech, data scientists often get heavier equity packages. So if you’re comparing base only, data engineering wins. If you’re comparing all-in at Google, data science might edge ahead. Depends on the company and how they structure comp.
The growth numbers are worth paying attention to. BLS projects data scientist roles growing 33.5% from 2024 to 2034. Fourth fastest-growing occupation in the entire country. Data engineering doesn’t have its own BLS category yet, which is honestly kind of ridiculous given the size of the field, but whatever, bureaucracy moves slow. Industry estimates put growth at roughly 23% year over year with about 150,000 data engineers currently employed and 20,000+ new hires last year.
Both safe bets. Both will keep paying well.
But data engineering has a specific structural advantage right now and it’s the thing I keep coming back to in conversations with hiring managers so I might as well explain it here. Companies rushed into AI. Hired data scientists. Started building models. Then realized the underlying data infrastructure was a disaster. Pipelines brittle. Warehouse held together with duct tape. The boring foundational work nobody wanted to invest in turned out to be, you know, foundational. So now everyone’s scrambling to backfill data engineering roles in a market where the good candidates already have three other offers. Every company that made the same sequencing mistake is scrambling at the same time. And that’s the dynamic, more than any macro trend, that keeps pushing data engineer salaries up faster than data scientist salaries.
What Hiring Managers Keep Getting Wrong
I’m going to be blunt about this because we have it three or four times a week.
Stale benchmarks. In a market growing this fast, comp data from twelve months ago is already wrong. Not a little wrong. Wrong enough to kill a search. You post the role, get zero qualified applicants, blame the market, call us confused. We look at the posting. You’re offering $115K for a role that costs $140K. Mystery solved.
Wide salary bands. I see postings that say “$80,000 to $160,000” and I know immediately the company hasn’t figured out what seniority level they need. Candidates know it too. Experienced engineers read a range that wide as either “they have no idea what this role is” or “they’re going to lowball whoever applies first.” Either reading means the good people close the tab and move on.
And then the Big Tech panic. Some mid-market companies can’t match FAANG base salaries so they treat that like the end of the conversation. It doesn’t have to be. Smaller companies win candidates from Big Tech all the time by leading with equity in a growing company, genuine flexibility (not the kind where you say remote but actually mean be-in-the-office-four-days), interesting problems, less bureaucracy, faster title movement. If your only pitch is the dollar number and your dollar number is $30K below Amazon, yeah, you’ll lose every time. But you probably have a better pitch than you think. You just haven’t figured out how to tell that story yet.
Last thing, and I probably shouldn’t say this because it’s bad for my business.
Not every hire needs a staffing agency. If your employer brand is strong and your process is fast and your comp is competitive, hire direct. Save the money. Where a staffing partner genuinely earns the fee is when the role has been sitting open for 60-plus days, when your pipeline dried up, or when you need a very specific combination like Kafka plus Snowflake plus HIPAA compliance in healthcare. That’s when specialized recruiting makes sense. If you can fill it yourself in two weeks? Do it. I’d rather be honest about that and have you call us when it’s real than pretend every data engineer hire requires an agency.
Data Engineer Salary by Industry
Same skills, same title, different check.
| Industry | Median Total Pay | Why |
|---|---|---|
| Information Technology | $142K+ | Tech invented the role. They still set the market. |
| Financial Services | $138K+ | Trading firms need real-time data. Compliance adds complexity and pay. |
| Energy and Utilities | $142K+ | Massive infrastructure buildout. Underserved talent pool. |
| Healthcare | $130K – $140K | HIPAA adds a premium for experienced people. |
| Retail / E-Commerce | $128K – $138K | Customer analytics and recommendation engines. |
| Media and Telecom | $138K – $140K | Streaming platforms. Content personalization. |
Glassdoor industry-level data, March 2026.
I already talked about energy in the city section but it’s worth repeating here because the industry premium and the geographic premium are compounding on each other. An energy company in Houston is paying both the industry premium for data engineers and the Houston premium that’s been driven up by all the other energy companies hiring data engineers in Houston. It feeds on itself.
Remote, Hybrid, and On-Site
Remember when everyone assumed remote work would flatten salaries across the board?
Didn’t happen.
Fully remote data engineering roles basically evaporated. The 365 Data Science analysis found remote-only postings dropped to about 2% in 2025. Two percent. Hybrid is the default now, with about half of U.S. tech workers splitting time. So most data engineer salaries are still anchored to a metro area even if you’re only going into the office two or three days.
Motion Recruitment’s 2026 data puts remote mid-level data engineers at $122K to $153K, which is competitive but noticeably below what the same person would make in a San Francisco or New York hybrid role. And some companies location-adjust. Some don’t. They’re not always upfront about which one they are, which is annoying. If you’re a candidate evaluating a remote offer, ask how they handle location-based comp before you get attached to the number on the letter.
If you’re a hiring manager thinking you’ll offer remote and save on salary, I have bad news. The talent still knows what they’re worth. Remote is a perk, not a discount. Trying to underpay by $20K because someone lives in Boise just means they take the other remote offer that doesn’t try to discount them for their zip code.
Certifications Worth the Time and Money
Certs don’t replace experience. Never have. But the right one bumps an offer by $5K to $15K and, maybe more importantly, gets a resume past automated screening. Big companies get thousands of applications. Recruiters literally search for “AWS Certified” or “Databricks” to cut the pile down. If you don’t have the keyword, you don’t make the cut. Simple as that.
- AWS Certified Data Analytics or Solutions Architect. Biggest cloud market share by a wide margin. Safest bet if you don’t know which platform to pick. Most postings that mention cloud mention AWS first.
- Google Professional Data Engineer. Narrower. Carries weight at GCP shops and more and more at companies running BigQuery. If your target companies aren’t on Google Cloud it won’t do much. Worth it if they are.
- Databricks Certified Data Engineer. Got completely overhauled recently. Enterprise Databricks adoption is growing fast enough that this cert went from nice-to-have to genuine differentiator in about eighteen months. Not a fad. The companies betting on Databricks are betting hard and they want people who already know the ecosystem because training someone from zero on a new platform while simultaneously trying to ship a production pipeline is a recipe for a six-month delay and everyone involved being miserable.
- Snowflake SnowPro Core. Company went from startup to $50-plus billion because the product actually works, which you cannot say about most enterprise data tools without laughing. Tons of companies standardizing on Snowflake for their warehouse layer.
- Microsoft Fabric (DP-600). Most salary guides skip this. Microsoft retired the old DP-203 Azure Data Engineer cert and replaced everything with Fabric-focused credentials. If you’re targeting enterprise, and especially finance, healthcare, or manufacturing, Microsoft certs often matter MORE than AWS or GCP. These organizations have decades of Microsoft investment baked into their infrastructure. They are not switching. Something like 97% of Fortune 500 companies use Power BI already and Fabric ties the whole stack together.
Pick one. Maybe two. Don’t try to collect all five. That’s expensive and pointless. Go with whatever your target companies run. If you genuinely don’t know, AWS is the safe default.
How to Actually Use This Data
Hiring managers. Take the experience-level table, adjust for your city and industry, and go have a conversation with finance before you post anything. Use our salary assistant for role-specific numbers. Nothing kills a data engineering search faster than an offer that lands $15K below what the candidate expected from their other conversations. You don’t get a second shot at that. They’re gone.
Candidates. Don’t trust any single source. I showed you earlier that PayScale says $99K and Glassdoor says $132K for the same title. Thirty-three thousand dollar gap. Cross-reference at least three. And if you have Spark or Kafka or Snowflake experience, you have way more room to push back on a number than you think. Most companies expect negotiation. The ones who don’t aren’t worth working for anyway.
Building a team? Hire data engineers first. Before data scientists. I know. Data scientists are more exciting to talk about in board meetings. But data engineers build the thing the models actually run on, and if you skip the infrastructure and go straight to hiring the people who build models, you’re going to spend six months wondering why nothing makes it to production and then hire the engineers anyway at a premium because now it’s urgent. We have a full playbook on how to hire data engineers and our data engineering staffing team can move faster when timing gets tight.
Frequently Asked Questions About Data Engineer Salary
What is the average data engineer salary in 2026?
Depends entirely on the source, which is part of the problem with this question. ZipRecruiter says $129,716. Glassdoor says $132,100. Indeed says $135,889. Built In says $125,978. PayScale says $99,737, which honestly seems low to us but their methodology skews toward early-career survey respondents so there’s a reason for it. Salary.com says $123,048. Put them all together and the realistic composite is $125K to $135K in base pay nationally, all experience levels blended.
How much do entry-level data engineers make?
$80K to $105K. Strong Python and SQL with some cloud exposure gets you $85K to $100K outside of major tech hubs. San Francisco or New York, add $15K to $25K.
Do data engineers make more than data scientists?
On base? Usually yes. BLS median for data scientists is $112,590 as of 2024. Data engineer medians sit $125K to $135K across most sources. But total comp at Big Tech sometimes flips it because data scientists tend to get heavier equity packages there. So “it depends” is the honest answer, which I know is annoying, but whether you’re comparing base-only at a mid-size company or all-in at Meta makes the answer completely different.
What city pays the highest data engineer salary?
San Francisco. $180,255 median total on Glassdoor with the 75th percentile at $232K. But Houston at $173K is the one nobody sees coming.
Is data engineering still in demand in 2026?
Not just in demand. One of the fastest-growing technical roles in the entire economy. The World Economic Forum’s 2025 Future of Jobs Report listed big data specialists as the fastest-growing job in technology, projected growth above 100% through 2030. The BLS projects 33.5% growth for the closely related data scientist category from 2024 to 2034, fourth fastest in the country. About 150,000 data engineers employed in the U.S. right now. Over 20,000 new hires last year alone. Global big data engineering services market on pace to roughly double to $187 billion by 2030. The question isn’t whether data engineers are in demand. The question is whether there are enough of them. From where we sit the answer is no and it’s not particularly close to being yes anytime soon.
What skills increase a data engineer’s salary the most?
Spark. Snowflake. Kafka. Deep AWS knowledge. Those four. Real-time streaming experience is the single biggest separator because batch-only engineers are a completely different talent pool. Databricks is climbing fast too. Eighteen months ago it was optional. Now it’s a hard requirement in about one out of six postings.
How does remote work affect data engineer pay?
Less than you’d think. Fully remote postings are down to about 2%. Hybrid is the default. Remote mid-level engineers earn $122K to $153K based on Motion Recruitment’s 2026 data, which is competitive but below top-metro hybrid roles. Some companies location-adjust, some don’t, and they’re not always honest about which one they are upfront. Ask early.
Should I hire a data engineer through a staffing agency?
Not always and I say that as someone who runs a staffing business. If your brand is strong, your process is fast, and your comp is right, hire direct. A specialized staffing partner earns the fee when the role has been open two months with no traction, when you need a specific combo like Kafka plus Snowflake plus HIPAA, or when you’re building a whole team on a deadline. We break down fee structures in a separate guide.