• 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.

• 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.