Data engineer, data scientist or analyst: who should your startup hire first?
At some point, every founder says it: "We need a data person."
It's usually true. It's just not a brief yet, because "data person" covers three very different jobs. A data engineer, a data analyst and a data scientist all work with data, but they solve different problems, and each one needs something in place before they can do their job well.
The short answer: hire for the problem you're trying to solve, not the title that sounds most impressive. If your data is scattered, hire a data engineer. If your data is in one place but nobody's turning it into answers, hire an analyst. If you have clean data at volume and a model is part of the product, that's when a data scientist makes sense.
The three roles, in plain English
Data engineer. Builds the plumbing. They get data out of your product, your tools and your spreadsheets into one place, and keep it flowing reliably so everyone is working from the same numbers.
Data analyst. Turns data into answers. Dashboards, reporting, and the "why did sign-ups drop last week?" questions that land on a founder's desk.
Data scientist. Builds models. Predictions, recommendations, pricing, fraud detection, and experiments that need proper statistical rigour.
Role | What they do | What needs to be in place first |
Data engineer | Moves data from every source into one place and keeps it reliable | A live product and data worth collecting |
Data analyst | Answers business questions with data | Data that's accessible, ideally in one place |
Data scientist | Builds models that predict, recommend or optimise | Clean, reliable data at volume, and a problem worth modelling |
Where analytics engineers fit. This is a newer role that sits between the engineer and the analyst. They take raw data and shape it into clean, documented tables that analysts can actually use. It's useful once engineering and analysis are both happening and the space between them has got messy. It's rarely a first hire.
Start with the problem, not the title
Before you decide on a role, work out what's actually stopping you. It's usually one of three things.
Your numbers don't add up. Two dashboards give you two different revenue figures, nobody is quite sure where a number came from, and someone loses hours every week exporting to spreadsheets. That's a data engineering problem. An analyst hired into that situation will spend their time cleaning data, not analysing it.
Questions are piling up. The data is reasonably accessible, but nobody has time to dig into it, and you're the one pulling numbers at 11pm before a board meeting. That's an analyst.
The product needs a model to work. Not "AI would be nice to have". The product genuinely has to predict, recommend or personalise. If that's you, and your data is ready, a data scientist is the right hire.
If more than one of these sounds familiar, fix them in that order. Each role builds on the one before it.
Why the order matters
Most startups build their data function in the right order: get the data flowing, then start answering questions, then build models. It's worth understanding why, because skipping a step is expensive.
Picture a seed-stage fintech with twelve people. Transactions live in the product database, marketing data sits across three different tools, and finance runs on spreadsheets. The founder wants to know which customers are worth acquiring, so they hire a data scientist first. Three months later, there are no models. The data scientist has spent the whole time trying to get the data into one place, which is a data engineer's job.
Nobody did anything wrong there, except the order. A data scientist without clean data ends up doing data engineering, often without the right experience for it and usually without much enthusiasm. That's a retention problem as much as a productivity one.
The fix isn't to avoid hiring a data scientist. It's to hire one when the foundations are there to let them do the job you hired them for.
What if you can only make one hire?
At seed stage, you probably can't justify three data roles, and you don't need to. The first data hire is often someone who can cover two of these jobs: an engineer who's comfortable doing analysis, or an analyst who can build and maintain simple pipelines.
Hire for your biggest problem first, and accept that one person won't be brilliant at everything.
When do you need a head of data?
Not for your first hire. A head of data makes sense once you have a small team working across engineering, analysis and modelling, and data matters enough to the business that someone needs to own the strategy, not just the work.
Hire the leader before the team exists and you're paying for strategy with nobody to deliver it.
Why we ask "why?" first
This is the conversation we have before we take on any data brief. Not "what title do you want?" but "what problem are you trying to solve?"
Sometimes the answer confirms the brief. Sometimes it changes it completely. Either way, it's the difference between a hire who makes an impact from day one and one who spends three months doing a job they weren't hired for.
If you're working out your first data hire, let's talk.
FAQ's
Can a data scientist do data engineering?
Some can, especially in small teams. But it's rarely what they want to spend their time on, and data engineering done as a side task tends to stay fragile. If your main problem is getting data into one place, hire for that problem
What's the difference between a data analyst and a data scientist?
An analyst explains what happened and why, through reporting and analysis. A data scientist builds models that predict what will happen or automate decisions. Analysts answer questions. Data scientists build systems that answer them repeatedly.
What is an analytics engineer?
A role between data engineering and analysis. They turn raw data into clean, documented tables that analysts and the wider business can rely on. Most startups don't need one until they have both engineers and analysts in place.
Do I need a head of data before building a data team?
Usually not. Early on, you need people doing the work. A head of data becomes worth it once there's a team to lead and a data strategy that affects the whole business.
