Senior Data Analyst

In your first few months, you might:

• Work out what drives account growth: which behaviours predict long-term retention and expansion, and how Sales and Customer Success can act on them.
• Map how our customers really use the product: which workflows stick, where new features find their audience, and what healthy adoption looks like in an account's first months.
• Pick a question nobody has asked yet, answer it, and change a product or go-to-market decision with the result.
• Build the dbt models and Looker views your analyses need, so the next person can answer the question themselves.

HOW WE WORK

• Questions Over Tickets: You don't wait for requests. You figure out what matters and go after it.
• Depth Over Dashboards: One analysis that changes a decision beats ten reports nobody reads.
• Pragmatic by Default: Simple processes, fast iterations, and the lightest method that gives a trustworthy answer.
• Clarity Wins: A finding is only as good as how well others understand it and act on it.
• AI-Native by Default: We work heavily with Claude Code and other AI tools and expect everyone to use them fluently.

YOUR PROFILE

• 5+ years in analytics at fast-growing startups or scaleups, ideally B2B SaaS, where your work shaped product or commercial decisions.
• An exceptional ability to reason through ambiguous business problems: framing the right question, choosing a sound approach, and landing on a clear recommendation.
• Hands-on experience in both product analytics (usage, adoption, engagement, experimentation) and commercial analytics (retention, expansion, funnel, pricing).
• A track record of driving your own analytical agenda with minimal guidance.
• Strong SQL and professional experience with dbt and a BI tool (a strong command of Looker is a big plus). You build the models and views you need instead of waiting for them.
• Solid Python for analysis and scripting.
• Strong, hands-on familiarity with AI tools, especially Claude Code, and a habit of using them in your daily work.
• Excellent written and spoken English. You can explain a complex finding in one clear paragraph.

BONUS

• Experience with marketplaces or two-sided platforms.
• Experience in vertical SaaS, construction tech or procurement.
• Experience with BigQuery.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung