Senior/Staff Data Analyst

About the role

You will partner with product and business teams to run rigorous analysis, including root cause, opportunity sizing, and funnel drop-off, translating findings into clear, prioritised recommendations that span shifting problem areas. You will define, document, and maintain precise metric definitions and business context so stakeholders and AI tools can correctly interpret performance indicators such as Transaction Success Rate and Fraud Rate. The role involves setting up tracking and monitoring for new launches and changes, quantifying their impact on KPIs, and building a reporting layer with automated alerting that flags unexpected movement and surfaces root cause. You will design, run, and interpret experiments, using A/B tests and quasi-experimental methods like difference-in-differences when randomisation is not feasible, ensuring robust evidence guides decisions. You will model trusted data in the dbt and BigQuery layer, safeguarding quality through testing, documentation, and certification in close collaboration with engineering. In addition, you will create reusable, AI-assisted analytics skills that scale the team's impact and enable Product and Engineering to self-serve reliable insights. You will communicate complex concepts and findings to both technical and non-technical audiences across different teams, ensuring clarity that drives critical business decisions. This role is for someone who enjoys turning complex data into clear direction, influencing decisions, and helping shape the future of high-impact financial and crypto products.

Key facts

What you'll 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 reliable answers.

Communicate complex concepts and findings to technical and non-technical audiences across different teams, while ensuring clarity and understanding to drive critical business decisions.

You will thrive by turning ambiguous situations into structured analysis plans that deliver measurable business outcomes.

You will translate business questions into metrics and frameworks that enable consistent reporting across the business.

You will collaborate with data engineers to ensure pipelines are reliable, performant, and well-documented for downstream consumption.

You will use AI-assisted tools to accelerate analysis while maintaining rigorous validation of outputs for accuracy and bias.

You will own the end-to-end analytical lifecycle from problem definition through insight generation and action tracking.

You will mentor other analysts and stakeholders on best practices for data literacy and interpretation.

Requirements

Must have 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 you.

Strong written and verbal communication skills to convey complex analytical concepts to both technical and non-technical stakeholders.

Ability to work cross-functionally with Product, Engineering, Design, and Finance in a fast-paced, high-accountability environment.

Practical notes

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

Travel: 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).

Visa: Case by case.