Data Engineer

We're looking for a Data Engineer to join our Information and AI Platform and own our data pipelines end to end — from data modeling to experimentation to production.

What you'll do

• Own our pipelines end-to-end, from stakeholder metric definition through dbt modeling, QA, and A/B testing, to Dagster scheduling and production.
• Move us from people asking for reports to a system that answers — self-service for coworkers, reliable answers for AI agents, a self-describing warehouse.
• Keep our steering metrics real and trusted as governed metrics-as-code.
• Run the relevance and content pipelines that keep the platform a reflection of real life.
• Work closely with Product, engineering, and other stakeholders to translate their data needs into models that hold up in production.
• Keep our data governance in good shape in terms of quality, security, and compliance.

What you'll need

• Experience in a data engineering role or similar, with a solid understanding of data warehousing and ETL concepts.
• Strong SQL skills and hands-on experience running dbt models in production.
• Experience with an orchestration tool — we use Dagster.
• You've built and run data or information infrastructure that other teams depend on.
• Experience with a cloud data warehouse, ideally Snowflake, and a cloud provider, ideally AWS.
• You're comfortable with version control, ideally Git.
• You're sound on GDPR and Article 9 in practice — you know how to design compliant data access for both people and agents.

Nice to have

• You've taken a ranking or recommendation pipeline through to production.
• You know your way around ML concepts — you don't need to build models yourself, but understanding the basics helps you work well with our ML engineers.
• You have experience or are interested in working with data agents and making them reliable.

What they offer

• Hybrid work model with at least 2 office days per week
• 30 days of vacation per year with workation and sabbatical options
• 35 EUR monthly SpenditCard for personal use
• Collaborative team with learning and development opportunities