About this role
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
Responsibilities
- 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.
- Experience in a data engineering role or similar, with a solid understanding of data warehousing and ETL concepts.
Requirements
- 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