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Finom is hiring a Lead Analytics Engineer for Finance. You will partner with treasury, reconciliation and accounting to shape data strategy, build scalable models and trusted data products, and drive automation across finance workflows.
You will define data standards, ensure data quality, and collaborate with corporate finance on investor reporting, while mentoring others in analytics‑engineering practice.
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
You will 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.
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.
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