The opportunity
Analytics Engineering makes GitLab's data trusted, easy to find, and ready for anyone to use, whether they ask a dashboard, Claude, or a Slack agent. We own the curated data models, the semantic layer, the self-service tools, and the data applications that let teams answer their own questions.
What you'll do
Build and own a trusted semantic model, with governed metrics and business: definitions, that answers business questions consistently across every tool
Build evals and ground-truth datasets that measure answer quality, and use: them to drive improvements and catch regressions
Instrument telemetry to see who uses the semantic model and AI tools, what they ask, and where they get stuck
Build a model-selection harness so GitLab can route work across LLMs and stay flexible on cost and quality
Own data modeling and curated datasets for self-service, with the ownership,: data contracts, testing, and documentation that make them AI-ready
Define the AI data access strategy: when to use BI, Claude, MCP servers, or Slack agents, with the governance and security to keep access trusted and reliable
What they're looking for
- Own the enterprise BI and self-service tools strategy (e.g., Tableau, Omni),: including evaluation, adoption, and cost
- Build self-service capabilities, such as Claude skills, documentation, and: guided workflows, that help people find and use data on their own
- Build and own company-wide data applications, from Claude-built single-page: apps to apps inside BI tools, that combine Snowflake data, API calls, AI-powered deep dives, and write-back to Postgres or Snowflake
- Set engineering standards for data apps (security, authentication, testing,: deployment, and monitoring) so they're reliable at company scale
