We are looking for a Senior Data Analyst to join our Identity team.

This is not a classic BI/dashboard role. You will work on large-scale identity resolution, AI scoring systems, data quality, and product decision-making.

We process 70+ TB of data and run 20M+ enrichment operations daily across multiple data sources.

You will work directly with Product and Engineering and own analytics for one of the core business domains.

🟢 What You’ll Do

• Build frameworks to measure data quality, match rates, AI scoring accuracy, ROI
• Analyze and improve LLM-based scoring systems together with engineers
• Compare external data providers and identify highest-value sources
• Investigate mismatched / conflicting data across systems
• Support internal teams with audience, campaign, and performance analysis
• Detect pipeline or provider quality issues early and drive fixes
• Turn ambiguous business questions into clear decisions
  

🟢 Requirements

• 5+ years as a Data Analyst, Analytics Engineer, or Data Scientist in a product team — not consultancy, not BI-only roles.
• Advanced SQL at TB scale — BigQuery, Snowflake, or equivalent warehouse. Can read and write complex queries on tens of terabytes without someone pre-modelling the data.
• Python for analysis at a working level — pandas, Jupyter, or equivalent. Can write a small script or helper when an existing tool doesn't cover the case.
• Built measurement or evaluation frameworks from scratch for systems with no baseline — candidate defined what to measure and how, not consumed someone else's KPI.
• Comfort with probabilistic systems — understands noise, ranking, scoring, and that 100% accuracy is not a goal.
• AI-native workflow — uses Claude Code, ChatGPT, Copilot, or similar AI tools daily in their process.
• English: B2+ (Upper-Intermediate) or higher.
  

🟢 Strong Plus

• LLM evaluation / prompt testing
• Identity graph / AdTech / MarTech experience
• Healthcare / pharma data experience
• A/B testing / experimentation
  

🟢 We Offer

• Fully remote
• Flexible hours (CET ±2 overlap)
• High ownership / direct impact
• AI-native environment
• Fast decisions, low bureaucracy