Director, Analytics Engineering

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The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software.

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An overview of this role

As Director, Analytics Engineering, you'll lead a team of 8 and report to the VP of Data & Insights. You set direction across the data organization, stay close enough to the work to guide technical decisions credibly, and partner with Data Engineering, Data Science, IT, and business leaders. You will have three mandates:

• Build trusted, AI-ready self-service. Deliver a semantic model that answers business questions correctly, with the telemetry, evals, and model flexibility to prove it and keep costs in check.

• Set the strategy for how GitLab accesses data. Own the data models, BI tools, and AI access patterns (Claude, MCP servers, Slack agents) that put trusted data in everyone's hands.

• Build data applications the whole company relies on. Ship production-grade apps that combine trusted data, APIs, and AI to answer questions dashboards can't.

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

• 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

• Set a long-range roadmap tied to company strategy, and a lightweight operating model that balances urgent stakeholder needs with investment in reusable data products and tech-debt reduction

• Hire, coach, and retain a high-performing team of 8: build career paths, develop leaders, distribute ownership, and recognize great work

• Act as customer zero for GitLab and GitLab Duo in data workflows, sharing feedback with Product and Engineering

What you'll bring&