At a Glance
- Tasks: Lead data initiatives for Finance, enhancing treasury, reconciliation, and accounting processes.
- Company: Fast-growing fintech company focused on innovative finance solutions.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Join a dynamic team with high autonomy and opportunities to drive meaningful change.
- Why this job: Make a real impact by transforming Finance data processes with cutting-edge technology.
- Qualifications: Experience in finance data roles, strong SQL skills, and knowledge of data modelling.
The predicted salary is between 63000 - 77000 £ per year.
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 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.
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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Lead Analytics Engineer - Finance Data (FinOps) employer: Finom
Finom is an exceptional employer that fosters a vibrant and innovative work culture, empowering employees to make a significant impact on the financial landscape for entrepreneurs. With a strong focus on personal and professional development, flexible working arrangements across Europe, and unique benefits like the Work & Swim program in Cyprus, Finom offers a rewarding environment where your contributions are valued and recognised. Join us to be part of a dynamic team that prioritises creativity and collaboration while enjoying the perks of a rapidly growing tech startup.
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We think this is how you could land Lead Analytics Engineer - Finance Data (FinOps)
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We think you need these skills to ace Lead Analytics Engineer - Finance Data (FinOps)
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Finom. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
