Data engineer salary in 2026: tech hubs, nationwide and remote
Market benchmarks updated July 2026
In 2026, a mid-level data engineer in the US earns $110k–$145k in base salary nationwide, $138k–$181k in major tech hubs and $105k–$138k for fully remote roles (tensionscore benchmarks updated July 2026, based on BLS OEWS, Robert Half, Stack Overflow Developer Survey 2025 (US), Glassdoor 2026 (PM, design, mobile), BLS ECEC Q4 2025 (employer cost), SHRM). Including recruiting and the cost of the search, the first year costs the employer $201k–$312k at the national level.
Base salary by seniority and location
Seniority
Major tech hubs
Nationwide
Remote
Junior
$106k–$138k
$85k–$110k
$81k–$105k
Mid-level
$138k–$181k
$110k–$145k
$105k–$138k
Senior
$181k–$225k
$145k–$180k
$138k–$171k
Lead
$225k–$281k
$180k–$225k
$171k–$214k
Annual base salary excluding bonus and equity, in thousands of dollars.
Time-to-hire and real first-year cost
Seniority
Median time-to-hire
Major tech hubs
Nationwide
Remote
Junior
3–6 months
$194k–$296k
$155k–$237k
$147k–$225k
Mid-level
3–6 months
$251k–$390k
$201k–$312k
$191k–$296k
Senior
4–7 months
$350k–$508k
$280k–$407k
$266k–$386k
Lead
4–7 months
$435k–$635k
$348k–$508k
$331k–$483k
First-year total = fully loaded salary + recruiting cost + cost of delay (months without the skill), in thousands of dollars.
How location changes the numbers
Major tech hubs: about 25% above the national figures.
Nationwide: in line with the national figures.
Remote: about 5% below the national figures.
Major tech hubs means high-cost metros such as the San Francisco Bay Area, New York or Seattle. These gaps are a coefficient applied to the national benchmark, not city-by-city surveys; time-to-hire does not change. Details in the methodology.
The real first-year cost, in detail
For a mid-level data engineer nationwide, the advertised salary is only the visible part:
Fully loaded salary$143,000 – $188,500
Recruitment cost$22,000 – $29,000
Cost of delay (months without the skill) (over a median time-to-hire of 3–6 months)$35,751 – $94,248
First-year total$200,751 – $311,748
Assumptions
Fully loaded salary = base salary × 1.3 (benefits and employer taxes, BLS employer-cost data).
Recruiting cost = 20% of annual base salary, the typical agency fee; hiring directly costs less but takes internal time.
Cost of delay = monthly loaded salary × months without the skill: the value the role should have produced during the search.
One word, three different jobs. A data engineer builds the pipelines that collect and clean your data. A data analyst turns it into answers — dashboards, reports, the numbers behind decisions. A data scientist builds models that predict or automate. They’re neighbors, not substitutes.
The most expensive mistake in data hiring happens before the ad is even posted: picking the wrong one of the three. Businesses routinely post “data scientist” when the actual need is an analyst who can build reliable reporting — then wonder why candidates are overqualified, overpriced and bored within six months.
A blunt rule of thumb: if your data is scattered and unreliable, you need an engineer first. If the data exists but nobody can answer questions with it, an analyst. Only when you have clean data and a real prediction problem does a data scientist earn their keep.
What moves the range
Which of the three jobs it is drives the price more than anything else: analysts sit at one level, engineers above them, and machine-learning-capable scientists at the top — the AI wave has pushed that last segment into severe scarcity. Within each job, seniority and cloud data stack experience move the number further.
Your data maturity also shows up in the price. Building on a clean modern stack is one job; untangling years of spreadsheets and half-connected systems is another, and the people who do it well know their worth. Describe your starting point honestly and you’ll attract candidates who’ve done exactly that.
Writing the job ad for this role
Name the job precisely — engineer, analyst or scientist — and describe the actual work: the data you have, where it lives, the questions you want answered or the pipelines to build. List your current tools, even if they’re just spreadsheets and a database; candidates would rather know. Avoid stapling all three roles into one “data person who does everything” ad — it signals the role is undefined and the hire will be alone against the whole problem.
Paste your developer job ad: score, real time-to-hire, first-year cost and what’s driving candidates away — free, in 30 seconds.
Based on market benchmarks updated July 2026, a mid-level data engineer earns $110k–$145k in base salary nationwide, $138k–$181k in major tech hubs and $105k–$138k for fully remote roles. Pay climbs with seniority: the per-level detail is in the table on this page.
Do data engineers earn more in tech hubs?
Yes: in major tech hubs, data engineer pay runs about 25% above the national figures, while fully remote roles pay about 5% less. Time-to-hire stays similar (3–6 months for a mid-level data engineer): competition for strong candidates is tough everywhere.
Do I need a data analyst, a data engineer or a data scientist?
Follow the bottleneck. Data scattered, unreliable or manually copied between systems: data engineer. Data available but questions going unanswered: data analyst. Clean data plus a genuine prediction or automation problem: data scientist. Most small businesses need the first two long before the third — and hiring in that order saves a painful, expensive false start.
Do I need machine learning or AI for my business data?
Less often than the hype suggests. Most of the value in a small business comes from reliable reporting: knowing your real margins, which customers churn, which products move. That’s analyst work on solid pipelines, no models required. Machine learning earns its cost when you have lots of clean historical data and a specific, repeated prediction to make.
Why are data candidates so expensive right now?
Demand has exploded across every industry while genuinely experienced profiles remain rare — especially engineers who can build reliable pipelines and anyone credible in machine learning. Being precise helps you here: a well-scoped analyst role costs distinctly less than a vague “data scientist” posting, and precise ads attract candidates who actually match the need.