Updated November 2026
Choose your first data hire by the first question the business needs answered, not by the title. If the question is how customers use the product, hire an analytics engineer; if data sits in systems nobody trusts, hire a data engineer; if the plan depends on machine learning in the product, hire a data lead who has shipped models before. A head of data usually comes later, once two or more of those needs exist at the same time.
Founders often open a "head of data" search because the board wants better reporting or an investor mentioned AI. Both are vague enough that the hire spends six months working out the job.
Instead, write down the single question you most need data to answer in the next two quarters. Most early questions fall into one of three groups, each pointing to a different person.
Take an illustrative 30-person Series A product-led SaaS company with a free tier and paid team plans. The founders asked for a head of data. When they wrote the question down, it was "which free users turn into paying teams, and what do they do in their first week?" That is product analytics, so the right first hire was an analytics engineer working with the product team.
The same budget can buy very different people, so check the hire against the question.
| First question | First hire | What they build in 90 days | Wrong hire for it |
|---|---|---|---|
| Product analytics | Analytics engineer | Clean event tracking, modelled core tables, a few trusted product metrics | A data scientist who wants to build models before events are tracked properly |
| Data platform | Data engineer | Pipelines into one warehouse, documented definitions, alerts when loads break | An analyst who builds dashboards on data that is still wrong |
| ML in the product | Data lead with production ML experience | An evaluation set, a first model live behind a fallback, an estimate of cost per prediction | A researcher with strong papers and no production deployments |
The second and third paths have their own guides: hiring a data engineer and hiring ML and AI engineers. The rest of this page follows the analytics path, where most startups should begin.
Before you hire, sketch the one dashboard you as CEO would open every Monday morning. Name the five or six numbers on it, who answers for each, and what you would do if it moved. If you cannot fill that page, the hire is premature.
Then use the same sketch in interviews. Give finalists your draft and ask what is wrong with it: which numbers are vanity metrics, which will be hard to define and what they would need to see before trusting any of them. Good candidates push back on at least one metric.
In the example company, the CEO's draft had eight numbers. The analytics engineer they hired cut it to five in the final interview and pointed out that "active users" was counted three different ways in the product, the billing system and the investor update. That told the founders more than the take-home task.
Build a job scorecard around your first question so the panel interviews for the same profile.
Benchmark pay against current data for your market. As an illustrative example, an analytics engineer on a base salary of $120,000 would cost $24,000 to $30,000 with a contingency recruiter charging 20 to 25%. On our fixed-fee bands, that role sits in the up to $150,000 band at $11,500.
For a head of data at an illustrative $170,000, the same contingency range would be $34,000 to $42,500, against our $15,900 to $21,900 band for roles above $150,000. All bands are on our pricing page.
We are a fixed-fee recruiting partner for funded startups and fast-growing teams across North America, Europe and APAC. We hire across engineering, product and leadership, and we have helped 500+ startups from Seed to Series D.
One dedicated recruiter runs your search end to end, starting with which question the hire has to answer. You see first screened candidates within 7 days, and our average is 33 days from kick-off to hire. Fees average 6 to 9% of salary, with part paid as an advance and the balance when your candidate accepts. If we don't deliver a shortlist of at least 3 qualified candidates within 30 days, you can claim the advance back in full.
In most cases it should not. Most early questions need clean tracking and trusted metrics first, which is analytics engineering work. A data scientist adds most once that foundation exists, unless ML is core to the product.
A startup needs a head of data once two or more data people work on different questions and someone has to set priorities, definitions and tooling.
An analytics engineer turns raw data into clean, modelled tables and trusted business metrics. A data engineer builds and runs the pipelines and infrastructure that move data from source systems into the warehouse.
Give each finalist a draft of the dashboard you would check every Monday and ask what is wrong with it. Their questions about definitions, owners and data sources reveal more than a coding test.
Worth a brief chat about your next hire? Book a free call.