Senior/Staff Data Analyst

About MoonPay

MoonPay is for builders with something to prove.

This isn't a "work on cool crypto stuff" company. It's a high-standards, high-velocity, high-accountability company building the operating system for value movement. If the internet moves information, we move value: crypto, stablecoins, tokenized assets, and whatever comes next. Four offerings make that real: fund, tokenize, trade, and spend. 30M+ customers and 500+ ecosystem partners run on us. Licensed in the U.S. Regulated across the UK, EU, Canada, and Australia.

AI is the default operating mode here. It's woven into every role, and we expect you to use it daily. It handles the manual work so you can deliver on what actually matters.

You'll thrive here if outcomes excite you more than process, if impact motivates you more than titles, and if you want hard problems, real ownership, and teammates who love winning, building, and doing it together.

The bar is high. The pace is real. We're building for what's next, for humans and agents.

Recent recognition:

Forbes' America's Best Startup Employers 2026 .

2nd in Crypto Services on Fortune's inaugural Crypto 100

The Sunday Times Best Places to Work two years running

Research has shown that women are less likely than men to apply for this role if they do not have experience in 100% of these areas. Please know that this list is indicative, and that we would still love to hear from you even if you feel that you are only a 75% match. Skills can be learned, diversity cannot.

Locations Supported 🌍

London, UK (primary)

Remote (Europe)

Relocation available: Case by case

Work pattern:

Remote across Europe, with hybrid working encouraged if you're near a Moonbase (around 2 to 3 days per week in the London office).

About the Opportunity

In this role, you'll help shape MoonPay's product and business decisions through data, combining deep analysis, experimentation, and strong stakeholder partnership to drive real impact.

You'll sit within our centralised Product Data team and work closely with Product and Engineering, owning analytics across the full product lifecycle, from defining problems and success metrics through to launching, measuring, and optimising products at scale.

Your work will directly influence product strategy, customer experience, and business health, helping teams make better, faster decisions through trusted insights and clear narratives. You'll also raise the leverage of the whole team by building reusable, AI-assisted analytics skills that let the wider org self-serve reliable answers.

This is a great opportunity for someone who enjoys turning complex data into clear direction, influencing decisions, and helping shape the future of high-impact financial and crypto products.

What You Will Do

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.

About You

Must-have experience and skills

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.

Nice-to-have experience

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.

Bonus Points

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).