Analytics Engineer
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Role details
Tech stack
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Job description
- Infrastructure & Automation: Set up and maintain Git workflows, code reviews, and deployments in GitLab, while managing automated ETLs and agents.
- Efficiency: Identify repetitive manual work across the team and automate it.
- Hands-on Analysis: Dive directly into the data when a project calls for it, balancing your time between building and analyzing. Understand the analytics you are building solutions for.
- AI Integration: Use AI coding assistants (like Cursor or Claude) as a normal, everyday part of your workflow.
- Collaboration: Work closely with infrastructure teams to build and scale new solutions.
- Having lots of fun!
Requirements
- Has strong proficiency in Python, PySpark, and advanced SQL.
- Can take a vague, messy problem and drive it to a working solution by planning, designing, and delivering independently.
- Has a solid grasp of software engineering fundamentals (Git, continuous integration, and the discipline to maintain a mostly one-person codebase).
- Demonstrates great judgement on how much engineering a problem deserves, always keeping analytical correctness as non-negotiable.
- Is comfortable working with our AWS stack (or has a background in GCP/Azure with a genuine willingness to learn AWS).
- Is quick to pick up new tools and can build across a wide variety of infrastructures, rather than leaning on just one familiar stack.
- Communicates clearly, particularly when translating technical concepts to non-engineers.
- Has analytics at heart and cares about the question behind the code and digs into why a number looks off before moving on.
- Bonus: Has experience or curiosity in building AI-powered applications.
Benefits & conditions
Why you’ll love being a Similarwebber:
- Impact : Work with the world’s leading digital intelligence platform and make a real difference.
- Innovation : Bring your ideas forward - we empower you to drive change.
- Hybrid work model: 3 days in our office and 2 days working from home.
- Our Office: Modern space in Prague’s DOCK IN area, stocked with snacks, drinks (we’re big fans of kombucha), fruit and lunches on Tuedays.
- Benefits: 5 weeks of vacation, birthday day off, 3 sick days, or Multisport
- Top-tier Hardware : MacBook Pro M3, dual monitors, standing desks.
- Team Events : Regular team-building activities, monthly birthday celebrations with cakes and drinks, baking Thursdays and happy hours.
- Equity : Share in Similarweb’s success.
- Career Growth : Explore any path - leadership, switching teams, or upskilling through coaching and learning programs.
About the company
At Similarweb, we’re not just another data company - we’re the leading digital intelligence platform used by global giants like Google, eBay, and Adidas. Our insights power the digital strategies of over 4,300 companies worldwide, and we’re growing fast. After going public on the New York Stock Exchange in 2021, we continue to break new ground, and now we’re expanding our dynamic team in Prague!
We’re looking for an Analytics Engineer to join the Data Enablement team and help build the crucial bridge between our data and our customer-facing organization. As the sole engineering role on the team, you will own the technical work end-to-end - from a rough sketch to something stable and running in production.
Why is this role crucial?
As the most trusted platform for measuring online behavior, our data is only as powerful as our ability to communicate it. Engineering here is in service of analysis. By right-sizing your engineering to the problem - knowing when to ship a quick fix in a day and when to architect a quarter-long project - you will empower our analysts to work faster and help our customer-facing teams communicate our data effectively. Your independence, reliability, and technical judgement will directly impact how Similarweb’s insights are delivered to the world.
What You’ll Be Doing Day-to-Day
- End-to-End Ownership: Own the Databricks development environment and project structure for the team.
- Tool Building: Build practical internal tools (including AI-assisted and automated tools) that speed up data investigation and help customer-facing teams communicate data.
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