Director of Analytics Engineering, GitLab

• 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
• Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement

Desirable

• 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
• 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