Lead Data Analyst

Lead Data Analyst

This team is the bridge between our data and our customer-facing organization. Two things sit with us, and they feed each other.

• Answering the data questions nobody else can. A customer’s numbers do not line up with what they see in their own analytics. A metric moved and the account team needs to explain why. These arrive as tickets from across the customer-facing organization, rarely well specified, and closing one means working through the data and the pipelines behind it.

• Making the next question unnecessary. We turn what we learn into automated tooling, agents and AI-assisted products, so those teams get to the explanation and the insight themselves.

Every investigation is knowledge, and the tooling is how it reaches the rest of the company. The team has three people: this role, a data analyst, and an analytics engineer.

• Roughly 80% hands-on analysis, 20% management. You will be in the data most days. Two direct reports come with the role, but management is not the majority of the job and is not meant to become it.

• Knowing which data to use. We are a data company with a very large number of datasets, and most questions can be approached from several of them. Choosing the right one, and understanding how it was produced and where its limits are, is the central analytical skill here.

• Owning the answer, not the ticket. These answers usually turn on how a metric is defined and measured, so precision matters: what goes back has to hold up when the customer asks the next question.

• Deciding when to stop answering and start building. You see the whole flow of incoming questions, so you are the one who judges which recurring one is worth automating away, and who shapes it with the analytics engineer.

• Suited to someone who wants to lead a small team without stepping away from the work, and who digs into why a number looks off before moving on.

What the job involves

• Running escalated data investigations end to end: reproducing the issue, tracing the number back to its source, and answering it.

• Occasionally joining customer calls on urgent data issues.

• Deciding what the team works on: what gets answered now, and what gets an owner and a deadline.

• Reporting what we find to the teams that produce our data, bringing them in with the evidence and the context they need.

• Setting how the team runs: intake, routing, ownership and response expectations, with enough shared knowledge that any of the three of you can pick up any investigation.

• Turning recurring questions into permanent answers: documentation, dashboards, and internal tools and agents built with the analytics engineer.

• Managing two people, including technical direction for the analytics engineer.

• Using AI assistants such as Claude or Cursor as a normal part of the daily workflow.

What the role needs

• Python is a must. On top of that, strong SQL or PySpark, and if PySpark is not there yet, the ability and willingness to pick it up quickly.

• Excellence at working out where a number came from: choosing the right dataset among many, understanding how it was built, and getting to the bottom of problems in systems you did not build.

• Working experience with a notebook environment, a cloud platform and Git is a must. Our stack is Databricks, AWS and GitLab and the stack will keep expanding as we automate