Your mission
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.
- 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.
- Work with the team to improve personalized recommendations and other ML algorithms.
Our stack: dbt, Snowflake, MotherDuck, Dagster, SQL, Python, AWS, GitHub Actions, Docker, VS Code
Your profile
Must-haves
- 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.
- You're comfortable working directly with backend and infrastructure engineers as well as non-technical stakeholders, and can translate between the two.
- Experience with a cloud data warehouse, ideally Snowflake, and a cloud provider, ideally AWS.
- You're comfortable with version control, ideally Git.
- You have experience working with AI coding tools like Claude Code, and know how to use them to move faster without cutting corners.
- 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-haves
- 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.
- You're fluent in modern data and AI tooling and agentic systems, and understand why curation and semantic context decide whether agents can be trusted.
- You've led or mentored other engineers, or you're ready to grow into that, with support from our Leadership & Coaching guild.
And you're the kind of person who...
- Communicates clearly, and can explain complex data concepts to technical and non-technical people alike.
- Keeps up with new tools and trends in the field, and likes sharing what they learn with the team.
- Values diversity, collaboration, and inclusion, and helps build a team where everyone feels welcome.
- Is a systems builder at heart — energized by building what makes everyone else faster and more certain, not by owning a queue.
- Takes ownership of unsolved problems, like safe AI access to information, rather than waiting for someone else to solve them.
