Updated October 2026
A strong startup data engineer job description names the numbers the hire will make trustworthy, the sources and warehouse they inherit, and the teams they serve first. It lists six-month outcomes rather than duties, keeps must-haves to six and shows salary and equity ranges up front.
A rule of thumb before you write it: hire the data engineer when the third person asks for the same number and gets a different answer. Two answers can be settled over coffee. Three means your definitions live in spreadsheets, and a data scientist hired then spends months on plumbing they were never hired for.
Our running example is an illustrative Series A fintech: about 30 people, a Postgres app database, Stripe, HubSpot and a dozen spreadsheets feeding the board pack.
About [Company]
[Company] builds [one sentence on the product] for [who the customer is]. We raised [round] from [investors] in [month, year] and have [number] people. Our data lives in [app database], [billing tool], [CRM] and too many spreadsheets.
The role
We're hiring our first data engineer to build the pipelines and warehouse every team reports from. You'll report to [CTO], work closely with [Finance lead] and [Product lead], and own the definitions of our core metrics.
What you'll do in your first 6 months
What you'll bring
Nice to have
What we offer
In the fintech, two lines changed. The first outcome named the board metrics, starting with active customers, and the cost outcome got a real budget after one surprise invoice. To start from notes instead, try the free job description builder; our data engineer interview questions test each line.
| Seed | Series A | Series B | |
|---|---|---|---|
| Hire or wait | Often early; a product engineer with strong SQL can cover it | Usually the first data hire, before a data scientist | Joins a small data team |
| Main job | Get core data into one place | Own metric definitions and quality | Scale pipelines, add streaming where it pays |
| Cost ownership | Tools that stay cheap at low volume | A monthly budget they track | Spend allocated by team |
Benchmark salary and equity against current data for your market.
| Outcome | What good looks like at 12 months | How you'll test it in interviews |
|---|---|---|
| Trusted core metrics | One definition each; board pack matches finance | Resolve two conflicting definitions live |
| Reliable pipelines | Alerts catch failures before users do | Ask about the last pipeline they broke |
| Data quality | Tests on every core model | Ask which tests they wrote first in the take-home |
| Warehouse cost | Within budget; biggest drivers known | Ask what their last bill was and what they cut |
| Data contracts | Engineers flag schema changes before release | Ask how they would stop a renamed column breaking finance |
| Days | Focus | By the end they should have |
|---|---|---|
| 1 to 30 | Map every source and spreadsheet that feeds a decision | A list of conflicting metric definitions |
| 31 to 60 | Agree definitions, land core sources, add tests | Core metrics served from one model |
| 61 to 90 | Data contract, cost budget, retire spreadsheets | The first board pack built from the warehouse |
Advertising for a data scientist instead. A modeller with no clean data builds pipelines reluctantly, then leaves. Our data scientist job description covers that later hire.
Leaving out cost. Warehouse bills grow quietly with every scheduled query. If cost isn't in the JD, candidates assume someone else watches it.
Writing a report queue. "Build reports for the business" attracts people who enjoy being busy. Say they own the definitions and you attract people who fix the cause.
We've helped 500+ startups from Seed to Series D across North America, Europe and APAC. We agree a low fixed fee upfront, averaging 6 to 9% of salary. Illustratively, on a $140,000 hire a 20 to 25% contingency fee would be $28,000 to $35,000; our fixed fee is $11,500 (see pricing).
One dedicated recruiter runs the search, with first screened candidates within 7 days. If we don't deliver a shortlist of at least 3 qualified candidates within 30 days, you can claim the advance back in full. Read our guide on how to hire a data engineer for a startup.
Usually the data engineer. A data scientist needs clean, agreed data, and building that is the engineer's job. The exception is a company whose product is a model and whose data is already in good shape.
A data engineer moves data from source systems into the warehouse and keeps it reliable. An analytics engineer turns that data into documented models for analysts. A first startup hire usually does both.
Some, at first, because trusted numbers are the point of the hire. Put the definitions and pipelines in the JD as the job, and treat one-off report requests as a sign the warehouse is not finished yet.
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