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Analytics Engineer (Regulatory Reporting)
Finom
Source: Headcount Labs · original listing ↗
Job data: Headcount Labs, ODbL 1.0. Job descriptions belong to their employers.
Source date: 11/3/2026
About this role
Analytics Engineer, Regulatory Reporting Finom is growing, and so is the set of reports we submit to regulators and external parties. As our Analytics Engineer for Regulatory Reporting, you'll be the embedded data partner for the Regulatory Reporting team. You'll run and develop the reports, build the data behind them and set the quality standards that make regulatory data run like clockwork. You'll be the engineer behind regulatory reporting and own its whole technical side, so the Business Analyst and Report Owners can focus on requirements and the regulatory content. You'll sit in the Central Data team and work with the Regulatory Reporting function every day. We treat regulatory reporting as a product: a reliable internal service, not just a compliance obligation. This role helps build it that way. What You Will Be Doing - Own the full engineering scope of regulatory reporting. That includes pipelines, data models, formats, quality checks and releases, and coordinating with the engineering, data platform and product teams whose data and systems feed the reports - Run recurring monthly and quarterly regulatory reports and data extractions, and keep them accurate and on time - Update existing reports and build new ones as requirements change. Many of them are being built at Finom for the first time, so iteration is part of the work - Build the data behind the reports: pipelines, governed datasets and semantic models on our lakehouse - Generate external formats, including XML, and support submissions - Set up business checks, data quality standards and alerting, so issues are caught before a report goes out - Work with the Business Analyst and Report Owners on reusable requirement templates, data standards and data contracts with engineering - Bring datasets to self-service level, so the reporting team gets answers without waiting on data How We Work You decide how to structure semantic models, standards, quality checks and pipelines. Report content is signed off together with the Business Analyst and Report Owners. Priorities are set by final business impact. You'll work on a modern stack, including Databricks and n8n, with the freedom to trial AI tools, vendors and open-source solutions. Who You Are - You have 3+ years of hands-on technical experience in data, in engineering, analytics engineering or technical analytics - You write strong, expert-level SQL - You write working Python, including generating files in different formats such as XML - You've worked with data pipelines and a data warehouse or lakehouse - You can take technical requirements, turn them into working reports and adjust existing ones - You're comfortable being the technical point of contact across teams and driving work that depends on others - You choose precision over speed when the two conflict - You want to own the data behind a process, not just close tickets - You're an engineer moving into analytics, or an analyst with solid engineering skills. Both work for us Nice to Have - Regulatory or external reporting experience - Experience with XML schemas, report exports or integrations with external recipients - dbt, Databricks or Spark - Background in fintech, banking, insurance, investments, trading or government projects Your First Year - First 3 months: you're onboarded on the Data team's tools and workflows and have delivered the immediate priorities - First 6 months: you can support every recurring regulatory report on your own - First 12 months: you own a set of reports end to end and work directly with a group of stakeholders - Beyond: you curate releases with full data governance, and the regulatory dataset works at self-service level The Team You'll join a small central data team that works with every function at Finom. We use a modern stack, you're free to try new tools, and 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.