Senior/Staff Data Analyst

MoonPay is hiring a Senior/Staff Data Analyst to shape product and business decisions through deep analysis, experimentation, and stakeholder partnership. The role owns analytics across the product lifecycle and builds trusted insights and reusable AI-assisted analytics capabilities.

Responsibilities:

• Partner with product and business teams to run rigorous analysis (root cause, opportunity sizing, funnel drop-off) and turn it into clear, prioritised recommendations, moving across problem areas as priorities shift
• Define, document, and maintain clear metric definitions and business context, so stakeholders can understand the performance of their area and AI tools can interpret our metrics correctly (e.g. Transaction Success Rate, Fraud Rate)
• Set up tracking and monitoring for new launches and changes, and quantify and measure their impact on KPIs
• Build and maintain a reporting layer, with automated alerting and root cause analysis that flags when KPIs move unexpectedly and why
• Design, run, and interpret experiments, from A/B tests to quasi-experimental methods like difference-in-differences when randomisation isn't possible
• Model robust, trusted data in our dbt and BigQuery layer, safeguarding its quality through testing, documentation, and certification in collaboration with engineering
• Create reusable, AI-assisted analytics skills that scale the team's impact and help Product and Engineering self-serve
• Communicate complex concepts and findings to technical and non-technical audiences across different teams, while ensuring clarity and understanding to drive critical business decisions

Requirements:

• 5+ years of hands-on experience as a data analyst or data scientist, preferably in a product-focused role
• Advanced SQL and data visualisation are second nature to you
• Hands-on experience building data models in a modern cloud warehouse and transformation framework (e.g. dbt with BigQuery, Snowflake), with a strong instinct for data quality
• A solid grasp of statistics and probability, including experiment design and interpretation (A/B and quasi-experimental methods such as difference-in-differences)
• A working understanding of how a payments or fintech business operates, including fraud, chargebacks, KYC/AML, and payment success rates
• Comfort using AI-assisted and LLM tools to accelerate analysis, and curiosity about building reusable skills that scale your impact
• Exceptional communication and stakeholder skills. You can distill complex findings for any audience up to C-level, and lead projects independently
• Familiarity with our stack (dbt, BigQuery, Looker, Python) and AI tooling such as Claude Code
• Hands-on financial crime, payments, or fraud analytics experience
• Experience working in start-ups or scale-ups
• A crypto-native perspective and experience with on-chain and blockchain data (e.g. Dune, Flipside, Chainalysis)
• Experience making data self-serve for non-analysts (e.g. semantic layers, metric stores)