Director, Analytics Engineering at GitLab

Job description

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. 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 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 Have 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 About the Team 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. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $139,200 — $285,600 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Requirements

Must-haves

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

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
About the Team
Benefits to support your health, finances, and well-being
Team Member Resource Groups
Equity Compensation & Employee Stock Purchase Plan
Growth and Development Fund
Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapipostgresci/cdllmsqlpythonreacttypescriptgo

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision

Paid Time Off

Parental Leave