Director, Analytics Engineering

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:

1. 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.
2. 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.
3. 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.

• 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

• 12+ years in analytics engineering, data engineering, or BI, including 5+ years leading teams and 2+ years managing managers or senior leads
• Deep expertise in data modeling and semantic layers (e.g., dbt Semantic Layer, Snowflake semantic views, LookML, Cube), and expert dbt, including testing and CI/CD across a multi-team project
• Hands-on experience building LLM-powered data access (text-to-SQL, agents, MCP servers), including evals and telemetry for answer quality, and advanced use of AI coding agents such as Claude Code
• Expert SQL and strong Snowflake experience; working knowledge of Python
• Strong data governance experience: data quality, lineage, certified datasets, and access controls (RBAC, row access and masking policies)
• Track record of leading cross-functional data initiatives from strategy through adoption and measurable business impact, including enterprise BI and self-service at scale
• Systems thinker who sees how models, pipelines, and architecture decisions connect, and designs for second-order effects
• Exceptional communicator who writes clear technical specs, sets a clear vision, makes trade-offs visible, and influences executives and peers across the company
• Pragmatic in ambiguity: makes sound decisions on emerging technology and ships the right next version rather than waiting for the ideal end state
• Able to use GitLab, thrive in an all-remote, asynchronous company, model GitLab's remote working competencies, and work in line with GitLab's values

• Experience with LLM routing or gateway tools and managing model costs
• Experience with Tableau, Omni, or a BI platform migration
• Experience with web app development (e.g., React, TypeScript) and Postgres
• Experience owning finance or revenue-critical data (e.g., ARR, consumption) where accuracy and auditability are non-negotiable

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.

AI is changing how people get to data. Questions that once went through an analyst now go straight to AI tools, so the answers are only as good as the data models and business definitions behind them. Our job is to make those answers correct, consistent, and measurable.