- Build a data foundation we can trust. Get our people data clean and reliable, own the quality across our systems, and move our reporting to ThoughtSpot together with the central Data & AI team. Very few People teams have ever done this properly
- Turn data into direction. Dig into where we lose time, what causes friction, and what really drives engagement and retention. Bring us ideas that come out of what you’ve found, and give our leaders something they can act on
- Connect people’s data to the business. Linked up with Finance it makes headcount planning and forecasting much sharper. Built into our leading and lagging indicators, it shows how our people connect to what the business is trying to achieve
- Prove what we build works. Set the success measures before we build something, then go back afterwards and check whether it was delivered. We’d rather find out quickly and change course than be right slowly
- Power the AI builds. Work with the People Product Lead on agents, automations and AI-powered people products. You’ll build the data layer they run on and measure what they’re worth. This part of the role grows as we go
Benefits
- Extended healthcare and insurance: We offer occupational healthcare and well-being programmes in all locations, including mental well-being coaching. The specific programmes might vary depending on your location
- Flexible paid time off: We offer a flexible paid time off policy, providing up to 5 weeks of annual vacation days and paid family leave (subject to country regulations). Additionally, you can benefit from hybrid or remote work options, promoting a healthy work-life balance
- Regular fun with your team: To spend other than work-related time with your teammates, you get a team activity budget for three quarters a year. The fourth quarter is reserved for a company-wide event
- Individual learning budget: You get a yearly learning budget to use for courses and other relevant learning opportunities that help you develop your skills
Strong SQL skills and experience with modern cloud data platforms, such as Snowflake (preferred), BigQuery, or Databricks. You’re comfortable working with large datasets and understanding how data moves from source systems into analytical modelsComfort with Git-based development workflows. Version control, branches, pull or merge requests, code review, and collaborative development are a normal part of how you workStrong experience with modern BI and analytics platforms, such as ThoughtSpot (preferred), Looker, or Omni. You’ve built dashboards and analytical experiences that people genuinely use, and you understand how the BI layer connects to the underlying data models and sourcesHands-on experience with dbt or similar analytics engineering practices. You know how to build maintainable transformation pipelines, structure models, test and document data, and manage dependencies between datasets3+ years of experience as a Data Analyst, taking messy raw data through to a clear insight and a recommendation. Experience embedded in a business function is even betterExperience working with sensitive data and analytics access controls. You understand concepts such as role-based access, row-level security, column-level security, data masking, and least-privilege access, and can apply them pragmatically when working with sensitive People data
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