Data Analyst

Pave Bank is a digital bank built for businesses that operate globally, with a client base concentrated in digital asset trading venues, payment providers, stablecoin issuers and fintechs. Our Commercial team owns the client relationship from first conversation through to ongoing revenue growth.

We are looking for a Data Analyst who will sit inside the Commercial team and turn client, transaction and pipeline data into decisions. This is not a reporting role where you wait for requests and return a spreadsheet. You will build the dashboards, the models and the internal tools that the Commercial team uses every day, and you will ship them yourself.

We expect you to work with AI as a default part of how you build. If you currently do everything by hand and see AI tooling as optional, this role will not suit you.

• Analyse client revenue, transaction volumes, payment corridors and product usage to show where growth is actually coming from

• 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 the movements behind the numbers

• Identify at risk clients through changes in activity patterns, and identify cross sell and upsell opportunities across the book

• Support pricing decisions with evidence, including scenario modelling for fee changes and volume tiers

• Prepare data for board, investor and management reporting, and be able to defend every number in it

• Design and own the the Commercial team's dashboard layer, covering revenue, pipeline, client activity and product adoption

• Move the team away from manual spreadsheets towards live, self serve reporting that people actually open

• Define metrics clearly and consistently, and document them so the definitions do not drift between teams

• Work with Operations, Finance, Compliance and Product to make sure the underlying data is trustworthy

• Build internal tools that solve real Commercial problems, for example client scoring, pricing calculators, lead enrichment, reporting automation and data quality checks

• Deploy and host those tools on Google Cloud, and keep them running

• Automate recurring manual work in the Commercial workflow instead of absorbing it

• Write clean, documented, maintainable code that someone else on the team can pick up

• Use Claude Code and similar agentic tools as your primary build environment for analysis, tooling and automation

• Build AI assisted workflows into Commercial processes, for example summarising client interactions, drafting first pass commercial documents and structuring unstructured data

• Set the standard for AI usage inside the Commercial team, share what works, and help colleagues adopt it

• Apply good judgement on where AI output needs human verification, particularly for anything client facing or regulatory

• Minimum 3 years in an analytical role, ideally in fintech, payments, banking or a data heavy commercial environment

• Strong SQL, comfortable writing complex queries against large transaction datasets without hand holding

• Python for analysis and for building small applications and services

• Hands on experience with Google Cloud, including deploying and hosting an application, for example Cloud Run, Cloud Functions, App Engine, BigQuery, Cloud Scheduler

• Demonstrated experience building dashboards in a modern BI tool, for example Looker Studio, Metabase, Tableau or Power BI

• Practical, daily use of AI coding tools. You should be able to walk us through something you built with Claude Code or an equivalent agent, and explain what you delegated and what you reviewed yourself

• Advanced spreadsheet skills, including modelling and Google Sheets automation

• Strong commercial instinct. You care about why the number moved, not only what it is

• Fluent professional English, written and spoken, is mandatory. Our working language is English and you will present analysis directly to senior stakeholders and clients. Georgian is an advantage but not a substitute

• Understanding of payments mechanics, including SWIFT, SEPA, correspondent banking, card scheme settlement or stablecoin flows

• Experience with dbt, Airflow or similar transformation and orchestration tooling

• Familiarity with CRM data models and pipeline analytics

• Front end skills sufficient to build a usable interface, for example React or Streamlit

• Exposure to digital assets or a regulated banking environment

• Small team, wide scope. You will own your work end to end rather than hand specifications to someone else

• AI first by default. We expect meaningful output volume from a small headcount, and AI tooling is how we get there

• Direct access to decision makers. Good analysis changes what we do, quickly

• Bias to shipping. A working tool used by the team beats a perfect design nobody uses