Lead Analytics Engineer

The problem solver.

The one who sees a messy data environment and knows exactly where to start. Not the person who waits for a clean brief before writing a model. The person who figures out what the question actually is, builds something that answers it properly, and leaves the codebase better than they found it.

You might be a Senior Data Engineer who's spent as much time in transformation logic as in pipelines. You might be a Senior Analytics Engineer who's been the go-to person for the hard problems. We don't care about the title. We care about whether you have the technical depth to work through ambiguous data problems from first principles, the rigour to build things that hold up under scale, and the ownership instinct to care about what your data does downstream.

The company

Healf is Europe's fastest-growing health platform. £100M+ revenue, 700k+ customers, FT1000 number one.

We curate the world's best wellbeing products across EAT, MOVE, MIND, and SLEEP. That's the first chapter.

The next chapter is harder and more interesting. We are building Wellbeing Intelligence, a system that learns what works, for whom, and why, from the behavioural data of over half a million regular customers. That system is only as good as what feeds it.

What you will own

• dbt transformation work, contributing to and improving our modelling layer, building reliable, well-tested models that analysts and ML engineers can trust
• Cloud warehouse performance, understanding how queries run, where costs come from, and how to improve both without being told to.
• Ingestion pipelines from source to warehouse, working with tools like Airbyte or Fivetran, understanding reliability, and knowing when something is about to break before it does.
• Data quality and trust, writing tests, catching issues early, and thinking carefully about what downstream consumers actually need from your data.
• Feature-ready datasets for ML, understanding how models consume data and building pipelines that give the ML team what they need to iterate fast.

What success looks like

Data models that analysts and ML engineers rely on without second-guessing.

A measurable improvement in data quality and pipeline reliability over your first six months.

The ability to pick up a hard, ambiguous data problem and return with a clear solution and clean implementation.

A working relationship with Product, Engineering, and ML where your data work directly accelerates their output.

Why you're Healf

You're technically strong and you know it, but you don't need the perfect environment to prove it. You do your best work when the problem is hard and the brief is loose.

You've worked with dbt and understand how transformation layers are structured. You don't need to have owned one end to end, but you know what good looks like and you'll push toward it.

You've worked with data at real scale, clickstream, event-driven, or high-volume production environments where the data actually matters to real users.

You're curious about what happens downstream from your work. You've thought about how analysts use your models, how ML teams consume your features, and you've built with those consumers in mind.

You raise the standard quietly, not through grand proposals, but through the quality of what you ship and the questions you ask when something doesn't feel right.

The deal

Meaningful equity. Direct access to the Head of Data and leadership from day one.

Modern stack, no legacy debt, and a data problem genuinely worth solving. Your work will have a direct line to product features, ML capabilities, and decisions that affect hundreds of thousands of customers.