GitLab izstrādā mākslīgajā intelektā balstītu DevSecOps platformu, kas vienā vidē apvieno programmatūras izstrādi, drošību un operācijas. Tās produkti atbalsta pirmkoda pārvaldību, ar mākslīgo intelektu papildinātu izstrādi, CI/CD automatizāciju, atbilstības nodrošināšanu un ievainojamību pārvaldību visā programmatūras piegādes dzīves ciklā. GitLab apkalpo organizācijas, kas vēlas koordinēt inženierijas, drošības un operāciju darbplūsmas, vienlaikus efektīvāk piegādājot programmatūru. Cilvēki, kuri meklē darba iespējas GitLab, var atrast ar programmatūras inženieriju, mākoņinfrastruktūru, kiberdrošību, mākslīgo intelektu, produktu izstrādi un citām vienoto platformu atbalstošām funkcijām saistītas pozīcijas.
Director of Analytics Engineering at GitLab (Remote, US)
Lead GitLab’s analytics engineering team and shape data strategy for its DevSecOps platform. Build trusted semantic models, AI-enabled data access, BI tools, and company-wide data applications.
Apraksts
- Lead an eight-person team reporting to the VP of Data & Insights.
- Set direction for the data organization and guide its technical decisions.
- Collaborate with Data Engineering, Data Science, IT, and business leaders.
- Develop and maintain a trusted semantic model with governed metrics and business definitions.
- Create evaluation and ground-truth datasets to assess answer quality and detect regressions.
- Track usage of the semantic model and AI tools through telemetry.
- Develop a model-selection system to route work among LLMs while balancing cost and quality.
- Manage data modeling and curated self-service datasets, including ownership, data contracts, testing, and documentation.
- Define AI data access across BI, Claude, MCP servers, and Slack agents.
- Set strategy for enterprise BI and self-service tools, covering evaluation, adoption, and cost.
- Develop self-service resources such as Claude skills, documentation, and guided workflows.
- Create company-wide data applications that combine Snowflake data, APIs, AI analysis, and write-back to Postgres or Snowflake.
- Establish engineering standards for application security, authentication, testing, deployment, and monitoring.
- Set a long-term roadmap and operating model that support company strategy.
- Hire, develop, and retain a high-performing team of eight.
- Use GitLab and GitLab Duo in data workflows as an internal early adopter, and share feedback with Product and Engineering.
Prasības
- Bring at least 12 years of experience in analytics engineering, data engineering, or BI.
- Have at least 5 years of team leadership experience.
- Have at least 2 years managing managers or senior leads.
- Bring deep data modeling and semantic layer expertise, such as dbt Semantic Layer, Snowflake semantic views, LookML, or Cube.
- Be highly proficient with dbt, including testing and CI/CD on multi-team projects.
- Have hands-on experience building LLM-based data access, including text-to-SQL, agents, MCP servers, evaluations, and answer-quality telemetry.
- Be an advanced user of AI coding agents such as Claude Code.
- Have expert SQL skills and strong Snowflake experience.
- Have working knowledge of Python.
- Bring strong data governance experience covering data quality, lineage, certified datasets, and access controls such as RBAC, row access, and masking policies.
- Show a record of leading cross-functional data initiatives from strategy through adoption and measurable business impact.
- Have experience with enterprise BI and self-service tools at scale.
- Be able to use GitLab, work effectively in an all-remote asynchronous company, model GitLab’s remote working competencies, and act in line with its values.
- Preferred: Experience with LLM routing or gateway tools and model cost management.
- Preferred: Experience with Tableau, Omni, or BI platform migration.
- Preferred: Experience developing web applications with tools such as React, TypeScript, and Postgres.
- Preferred: Experience owning finance- or revenue-critical data.
Priekšrocības
- Benefits supporting health, finances, and well-being.
- Flexible paid time off.
- Team Member Resource Groups.
- Equity compensation and an employee stock purchase plan.
- Growth and Development Fund.
- Parental leave.
- Remote work for all roles.
- AI integrated into daily workflows as a productivity tool.