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.

• 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.

• 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 more, so picking up new tools matters.
• Enough software engineering to lead an engineer: version control, code review, pipelines and deployment. You will not write production code every day, but you do have to review plans, weigh trade-offs and set direction.
• Leadership experience, and the wish to keep growing in it. The formal side comes with the role: objectives, feedback and development for two people.
• Judgement about how far to take an investigation: some deserve a day, some two weeks. Analytical correctness is non-negotiable.
• The ability to explain data to people who are not technical, including customers, precisely and without hiding behind the detail.
• Ownership in ambiguity. Problems arrive without a specification, often without an obvious owner, and with someone already waiting on the answer.

• Experience with modelled or estimated data, such as panel data, web analytics or forecasting, where the method has to be explained as well as the number.
• Experience building or specifying AI-powered internal tools.

Why you’ll love being a Similarwebber:

• Impact😎: Work with the most powerful digital intelligence platform in the world, loved by both customers and employees.
• Innovation🚀: We value open dialogue and empower everyone to bring their ideas forward. You'll have the support and resources to take initiative and drive real change
• Our Office: Join us in our modern, newly designed office in the vibrant DOCK IN area (Prague 8). It's built to inspire focus and collaboration, stocked with snacks, drinks, fruits, and plenty of space for socializing. 
• Comprehensive Benefits: We offer 5 weeks of vacation, an extra day off during your birthday month, 3 sick days or a multisport card 😎
• Top-tier hardware👩🏻‍💻: We prioritize a great developer experience—you'll work with a MacBook Pro M3, dual monitors, and electric standing desk.
• Team events: At Similarweb, we love community! That’s why we host team-building events, parties, weekly lunches, and happy hours throughout the year 🎉.
• Access to Equity programs💸: Join in the success of Similarweb by becoming a shareholder!
• Grow your career any way you choose: Want to become a VP or switch departments? With Career Week, personalized coaching, and our ongoing learning solutions, you’ll find everything you need to grow your career right here!🚀.

#LI-BB1

#LI-Hybrid