About this role
About the opportunity
The Analytics Engineering & Governance Enablement (AEGE) team is part of N26's wider Analytics Engineering & Data Governance (AEDG) function. Our function covers the full spectrum from data modelling and transformation through to BI tooling and governance infrastructure that makes all of that work reliable at scale. Our Team AEGE sits at the enabling end of that spectrum: We build the standards and tooling that the broader analytics engineering community depends on, to ship trusted data products.
About the team: we build and operate the infrastructure that enables a cross-functional analytics engineering function to work reliably and at scale. We own the dbt services powering N26's core data warehouse and lakehouse - its CI/CD pipelines, runtime configuration, data quality standards, and the tooling that keeps a broad community of data producers, analytics engineers, data analysts, and data scientists productive. We care deeply about what "good" looks like in data products, and we build the systems and practices that raise that bar across the organisation.
In this role, you will:
• Own and evolve our core dbt services - maintain CI/CD infrastructure, drive performance improvements, enforce quality standards, and support a large community of data practitioners building on top of it
• Be the engineering interface for the analytics engineering community - unblock practitioners, review and improve shared patterns, and raise the bar on project quality, model structure, and SQL craft
• Improve data quality and governance infrastructure - contribute to services covering anomaly detection, freshness monitoring, drift detection, and data contract enforcement
• Shape data governance practices - drive standards around data ownership, data product thinking, and contract-first design across the data mesh
• Contribute to our Python based tooling - feature extensions, integrations, and observability utilities; write well-tested, maintainable Python that practitioners can build on
• Stay close to the platform - collaborate with our data platform team on the direction of shared infrastructure, representing the analytics perspective in technical decisions
What you need to be successful:
• Problem solving - you scope problems quickly, find root causes rather than treating symptoms. You know when to ship a pragmatic fix and when to go deeper
• Solid Python - typed, tested, maintainable code; you can contribute to shared tooling
• Strong SQL - you can read and reason about query plans, identify performance bottlenecks, and set a high standard for the SQL that goes into production
• Community and craft orientation - you're energised by helping others do better work; you can influence without authority and make good practices stick
• Track record of impact - you've improved reliability, quality, or productivity in a data organisation and can speak concretely about what changed
• dbt at scale - macros, hooks, incremental