Lead Analytics Engineer - Finance Data (FinOps)

About Finom Finom is growing fast, and Finance needs data that grows with it. As our Lead Analytics Engineer for Finance, you'll be the data partner for treasury, reconciliation and accounting. You'll also support our corporate finance and investor reporting work together with the team's existing data partner. You'll build and scale the data model behind these areas, set standards, integrate the systems Finance relies on and bring in automation, including agents, where it saves real work

This isn't a "build what's in the ticket" role. You'll learn how Finance works and why, understand where the value is, and work with the team on how their processes run, proposing improvements and the data and tools to support them. You'll report to the Head of Central Data

What You Will Be Doing - Partner with the Treasury, Reconciliation and Accounting teams on their processes, priorities and how data supports them. - Build out treasury data: positions, cash flows and liquidity, as trusted data products the team can rely on day to day. - Build and scale reconciliation, so that matching, breaks and exceptions are handled systematically rather than by hand. - Support Accounting with the data, checks and integrations behind their close and day-to-day work. - Support our corporate finance data partner on corporate finance and investor reporting, working from one shared model and set of definitions. - Design and evolve the Finance data model, entities and metrics, with tests, lineage and documentation, so the team can reuse and extend them. - Set data standards and quality checks for Finance data, and build integrations with the systems Finance uses. - Bring automation and agents into Finance workflows where they remove recurring manual work. - Look at how Finance processes run today, identify what can work better and connect each piece of data work to its business cost and value.

How We Work You'll have high autonomy. You choose the tools and the approach, including trialling new vendors and open-source solutions. Sign-off is needed in two cases. The first is architecture: whether a solution scales, holds across use cases and growing volumes, and runs without constant support. The second is anything that feeds high-stakes decisions. Priorities are set by final business impact.

Who You Are - You've worked in or closely with Finance: in a finance team, as a finance data partner, or in data work centred on treasury, reconciliation, accounting or FP&A. - You understand how a finance function works: the close, cash and liquidity, reconciliations and the reports that come out of them. - You write expert SQL and have strong dimensional or semantic data modelling skills. - You've built metrics and data products that other teams depend on. - You have ETL and data warehouse or lakehouse experience, including onboarding sources end to end. - You choose precision over speed when the two conflict. - You work like a consultant to the business: you understand how a process works, propose solutions and frame them in terms of business value.

Nice to Have - Databricks and Unity Catalog - Experience integrating ERP, accounting, banking or treasury systems - Experience with workflow automation or agents in finance processes - CI/CD, orchestration and infrastructure-as-code - Background in fintech, banking or payments

How You'll Grow In your first months you get to know Finance, the model, the stack and your stakeholders. By six months you're self-sufficient and make judgement calls on scope and design without escalating. From there, you set long-term goals together with your Finance stakeholders, own the data roadmap for treasury, reconciliation and accounting, and mentor others on analytics-engineering practice.

The Team We're a small central team that works with every function, and Finance is our primary partner. We use a modern stack, you're free to try new tools, and there are incentives for driving change. AI assistants take on routine work so people can focus on harder problems. We work in English, think long term, and build data that functions keep developing after we deliver it

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