Director, Analytics Engineering

GitLab

1d•$139,200 - $285,600•Remote

About The Position

GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100 trust GitLab to ship better, more secure software faster. 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. Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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