Hiring Your First Data Engineer: Skills, Interview Tasks and a Sample Take-Home

At some point every growing company hits the same wall. The product team wants a dashboard that shows weekly active users by plan. Finance wants revenue numbers that match the payment provider. Marketing wants to know which campaigns brought in customers who stayed. And all of it lives in five different tools, three spreadsheets and a production database that nobody wants to query during business hours.

That is usually when someone says, “We need a data engineer.” They are probably right. But hiring the first one is different from hiring the fifth, and it is easy to get wrong. This guide covers what to look for, how to interview, and a take-home exercise you can adapt.

What your first data engineer actually does

The first data engineer is rarely a pure specialist. In practice they spend their first six months doing three things: getting data out of source systems, modelling it into tables people can trust, and teaching the rest of the company how to use it.

A typical first-year roadmap looks like this:

1. Months 1–3: build the foundation
   • Set up a warehouse, often BigQuery, Snowflake or Postgres
   • Connect the top three or four sources, such as the app database, payments and the CRM
   • Write the first models for customers, subscriptions and revenue
2. Months 4–6: make it reliable
   • Add tests, alerts and documentation
   • Agree on one definition of “active user” with product and finance
3. Months 7–12: scale access
   • Hand self-serve dashboards to non-technical teams
   • Start planning the second data hire

That roadmap tells you what the role needs. It is less about distributed systems and more about SQL, data modelling, reliability and communication.

The skills that matter (and the ones that don’t, yet)

Job descriptions for data engineers often read like a list of every tool in the ecosystem. For a first hire, most of that list is noise. Here is how we suggest weighting the skills.

Skill	Weight for first hire	How to test it	Time in interview
SQL and data modelling	High	Take-home plus review	2 hours plus 45 min
Python for pipelines	Medium	Pair session	45 min
Stakeholder communication	High	Scenario interview	30 min
Streaming (Kafka, Flink)	Low	Not tested	0 min
Cloud infrastructure	Medium	Discussion only	15 min

Streaming is a good example of a skill that looks impressive but rarely matters early. Unless you are processing events in real time for a product feature, nightly or hourly batch jobs will cover your needs for at least the first two years. Hiring for streaming experience narrows your candidate pool without improving the outcome.

Salary expectations

Data engineering pay has risen steadily, and the first hire often needs to be fairly senior because there is nobody to learn from. Based on HireWise postings from the first half of 2026, mid-to-senior data engineers in major hubs earn roughly:

• San Francisco: $175,000 to $210,000 base
• London: £75,000 to £95,000
• Berlin and Amsterdam: €72,000 to €90,000
• Toronto: CA$125,000 to CA$155,000
• Remote, global rate: often pegged to a US or Western European band

Our full 2026 tech salary report breaks these numbers down by city and level. If your budget is closer to the bottom of these ranges, consider hiring an analytics engineer with strong SQL instead. Many of them grow into the broader role within a year.

Structuring the interview loop

A good loop for a first data engineer takes four to five hours of the candidate’s time in total, spread across no more than two weeks. Anything longer and strong candidates drop out. In our data, loops that ran past 21 days lost 27% of candidates before the final stage.

Stage 1: recruiter or founder screen (30 minutes)

Cover salary expectations, notice period and the basics of the role. Be clear that this is the first data hire, with all the freedom and the lack of support that implies. Some candidates love that. Others prefer a team with established practices, and it is better to find out early.

Stage 2: take-home exercise (2 hours, capped)

A short, realistic SQL exercise with a fixed time cap. Details and a sample are below.

Stage 3: take-home review and pair session (90 minutes)

Spend 45 minutes walking through the candidate’s submission together. Ask why they chose certain approaches and what they would do with more time. Then spend 45 minutes pairing on a small Python task, such as writing a function that loads a CSV into a table and handles duplicate rows.

Stage 4: stakeholder scenario (30 minutes)

Role-play a conversation with a product manager who wants a metric that the data cannot support yet. You are looking for someone who can say no politely and offer an alternative.

“The best data engineer I ever hired was not the one with the longest list of tools. She was the one who asked, during the take-home review, what decisions the finance team would actually make with the revenue table. That question saved us months.”

Marcus Lindqvist, CTO at Fernway Health

A sample take-home exercise

The exercise below uses three small tables from a fictional subscription business. It is designed to take about two hours and to test modelling judgement as much as SQL syntax. Send it with a clear deadline, for example “by Friday 17:00 CET”, and pay candidates for their time if your budget allows. Several companies we spoke with offered €150 or $150 per completed take-home.

-- Source tables (provided as CSV files)
-- customers(customer_id, signup_date, country, plan)
-- subscriptions(subscription_id, customer_id, start_date, end_date, monthly_price)
-- payments(payment_id, subscription_id, paid_at, amount, status)

-- Task 1: Monthly recurring revenue (MRR) by month for 2025
-- Only count subscriptions active on the last day of each month.

-- Task 2: Write a model that flags customers whose payments
-- have failed twice in a row. Include the date of the second failure.

-- Task 3: Short answer (max 200 words, in a comment):
-- What tests would you add to these models before trusting them?

-- Example starting point for Task 1:
WITH month_ends AS (
  SELECT
    (date_trunc('month', d) + interval '1 month - 1 day')::date AS month_end
  FROM generate_series('2025-01-01'::date, '2025-12-01'::date, interval '1 month') AS d
)
SELECT
  m.month_end,
  SUM(s.monthly_price) AS mrr
FROM month_ends AS m
JOIN subscriptions AS s
  ON s.start_date <= m.month_end
 AND (s.end_date IS NULL OR s.end_date > m.month_end)
GROUP BY m.month_end
ORDER BY m.month_end;

How to score it

Agree on a rubric before you send the exercise, so every reviewer looks for the same things. We suggest scoring each area from 1 to 4.

• Correctness: does the MRR query handle subscriptions that end mid-month?
• Edge cases: did the candidate notice refunds, null end dates or duplicate payments?
  • Bonus if they asked about currencies, since monthly_price might mix EUR and USD
• Readability: clear naming, comments where the logic is not obvious
• Testing mindset: the short answer in Task 3 often tells you more than the SQL

A candidate who scores 3 or above on correctness and testing mindset is usually worth moving forward, even if their syntax is a bit rough.

Red flags worth taking seriously

Some warning signs show up again and again in reviews. A submission that answers every task but never questions the data is one. Real source tables are messy, and a first data engineer has to notice that before a finance report goes to the board. Another is a candidate who proposes a large new stack on day one, such as a streaming platform and three orchestration tools, for a company that runs 40 queries a day. Ambition is good, but the first hire needs to match tools to the size of the problem.

Finally, pay attention to how candidates talk about past colleagues in analytics and product. Your first data engineer will spend at least a third of their week answering questions from people who do not write SQL. Someone who sounds impatient with those questions in an interview will probably sound the same on a Monday morning at 09:15 CET.

Writing the job posting

Be specific about the stack you have today and honest about what does not exist yet. “You will choose our warehouse” is attractive to some candidates and alarming to others, and both reactions are useful. Include the salary range, the interview stages and the expected timeline, such as “final interviews in the week of 2026-10-12, start date in November.”

If you have not posted a role on HireWise before, the guide to posting your first job walks through the form, and you can submit a job here when you are ready. For inspiration, look at how Supabase writes its engineering roles, or browse current data engineering jobs to see what other companies are offering. Employer plans start at $99 per month, and the pricing page compares monthly, quarterly and yearly billing.