Director, Analytics Engineering New Remote, United States

GitLab Inc.; United States; Hybrid; USD 180,000 - 240,000; Full time

Job summary

GitLab is seeking a Director, Analytics Engineering to lead a team of 8 and partner with senior leaders. You will shape data strategy, build AI-ready data platforms, and enable trusted self-service analytics across the company.

You'll own semantic modeling, data governance, and enterprise BI tools, driving cost-effective AI data access and scalable data products. This role requires strong leadership and hands-on expertise in dbt, Snowflake, and SQL.

Qualifications

• 12+ years in analytics engineering, data engineering, or BI, incl. 5+ years leading teams
• Deep expertise in data modeling and semantic layers (dbt Semantic Layer, Snowflake semantic views, LookML, Cube)
• Hands-on experience building LLM-powered data access, incl. evals and telemetry
• Expert SQL and strong Snowflake experience; working knowledge of Python
• Strong data governance: data quality, lineage, access controls
• Track record of leading cross-functional data initiatives from strategy to adoption
• Systems thinker who designs for second-order effects and scalable data products

Responsibilities

• Build and own a trusted semantic model with governed metrics across tools
• Develop evals and ground-truth datasets to measure answer quality
• Instrument telemetry to see how semantic models and AI tools are used
• Build a model-selection harness to route work across LLMs and control costs
• Own data modeling and curated datasets for self-service with governance
• Define AI data access strategy for BI, Claude, MCP servers, or Slack agents
• Own enterprise BI and self-service tools strategy (Tableau, Omni)
• Build self-service capabilities like Claude skills and guided workflows
• Create data apps combining Snowflake data, APIs, and AI with write-back
• Set engineering standards for data apps: security, testing, deployment, monitoring
• Set a long-range roadmap aligned with company strategy
• Hire and coach a high-performing team of 8 and recognize great work
• Act as customer zero for GitLab and GitLab Duo in data workflows

Skills

Analytics engineering

Data modeling

Semantic layer

dbt Semantic Layer

Snowflake

SQL

Python

AI data access

Tools

Snowflake

dbt

LookML

Tableau

Omni

API integration

Slack agents

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 valuesand 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 theUnited 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.

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.

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As set forth in GitLab’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.

If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:

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