[Hiring] Lead Analytics Engineer @Finom

Role Description

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 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.

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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.