Senior Platform Engineering

We are hiring a Senior Platform Engineer (m/f/d) to become the second senior voice in Platform, alongside our DevOps Lead, and to bridge two worlds he cannot cover alone: application-level developer experience and data engineering.

You will build the automation foundation for the manual data quality checks a single team member has held for years and is now transitioning out of. You keep our analytics layer running while planning its successor. You raise developer experience as a measurable discipline. You are hands-on, you write code daily, and you set the standard for how the engineering group builds and ships.

What you'll do

• Own Application & DevEx in the Platform group: developer frameworks, tooling, framework upgrades (NestJS, TypeScript ecosystem), test infrastructure, local dev flow, build pipelines.
• Build the automation foundation for Anja's manual data quality checks, pairing with Ghaith and Ghassen who own the check logic.
• Bridge DevOps and data engineering. Cover the data layer Hugo does not: Snowflake evolution, product data structures, data tooling landscape.
• Keep CubeJS running while architecting the migration off it, in partnership with the Data team.
• Raise developer experience as a discipline. Measure build times, test flakiness, PR turnaround. Reduce them.
• Cover GDPR posture on the platform side, either directly or in close partnership with whoever the founders bring in for compliance.
• Participate in the on-call rotation.
• Mentor across squads through code and design reviews. Ship golden paths, templates, and docs that reduce dependence on you.

You'll thrive in this role if you

• Have 8+ years in software engineering with 3+ years in a senior IC role owning developer platform, developer experience, or data platform work.
• Have straddled backend and data engineering deliberately. Fluent TypeScript / NestJS, comfortable in Python, experience with a data warehouse (Snowflake, BigQuery, Redshift, or equivalent).
• Have owned DevEx as a first-class product with measurable outcomes: build times, test flakiness, PR turnaround.
• Have taken over a system from a single point of failure. You know how to do knowledge transfer well.
• Have opinions about test infrastructure at scale and can defend the choices you made in a real codebase.
• Have shipped data quality automation: Great Expectations, dbt tests, custom frameworks. You know invariants matter more than assertions.
• Are AI-native in your engineering practice (Claude Code, Cursor, or equivalent) and have views on where AI helps and where it does not.
• Communicate in writing first and are comfortable with an ambiguous, broad charter you will shape as much as inherit.