data analyst in fintech

Pave Bank is a digital bank built for businesses operating globally, serving digital asset trading venues, payment providers, stablecoin issuers and fintechs.

• Analyse client revenue, transaction volumes, payment corridors and product usage to identify growth drivers;
• Build and maintain client profitability and unit economics models, including fee income, correspondent costs and FX spreads;
• Track pipeline health, conversion rates, time to onboard and time to first transaction, and explain changes in the metrics;
• Identify at-risk clients through activity patterns and find cross-sell and upsell opportunities;
• Support pricing decisions with evidence and scenario modelling for fee changes and volume tiers;
• Prepare data for board, investor and management reporting and defend every number;
• Design and own the Commercial team's dashboard layer for revenue, pipeline, client activity and product adoption;
• Replace manual spreadsheets with live, self-serve reporting;
• Define and document metrics consistently across teams;
• Work with Operations, Finance, Compliance and Product to ensure trustworthy underlying data;
• Build internal tools for client scoring, pricing calculations, lead enrichment, reporting automation and data quality checks;
• Deploy and host tools on Google Cloud and keep them running;
• Automate recurring manual work in the Commercial workflow;
• Write clean, documented and maintainable code;
• Use Claude Code and similar agentic tools for analysis, tooling and automation;
• Build AI-assisted workflows for summarising client interactions, drafting commercial documents and structuring unstructured data;
• Set the standard for AI usage in the Commercial team, share effective practices and help colleagues adopt them;
• Apply human verification where AI output requires review, especially for client-facing or regulatory work.

• At least 3 years of experience in an analytical role, ideally in fintech, payments, banking or a data-heavy commercial environment;
• Strong SQL skills and ability to write complex queries against large transaction datasets independently;
• Python for analysis and building small applications and services;
• Hands-on Google Cloud experience, including deploying and hosting applications with tools such as Cloud Run, Cloud Functions, App Engine, BigQuery or Cloud Scheduler;
• Experience building dashboards in a modern BI tool such as Looker Studio, Metabase, Tableau or Power BI;
• Daily practical use of AI coding tools, with the ability to explain what was delegated and personally reviewed;
• Advanced spreadsheet skills, including modelling and Google Sheets automation;
• Strong commercial instinct and focus on explaining why metrics change;
• Fluent professional English, written and spoken, is mandatory;
• Nice to have: understanding of payments mechanics including SWIFT, SEPA, correspondent banking, card scheme settlement or stablecoin flows, experience with dbt, Airflow or similar tooling, familiarity with CRM data models and pipeline analytics, front-end skills with React or Streamlit, exposure to digital assets or a regulated banking environment.

• No conditions specified