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GitLab is seeking a Staff Data Analyst to drive data-informed decision-making across the Support Engineering organization. In this role, you will transform raw data into actionable insights that inform strategic decisions, design dashboards and reports, and build data models that help leaders monitor performance, optimize costs, and improve customer outcomes.
Key responsibilities include:
- Own annual operating plan and capacity planning through data modeling and validation supporting workforce and budget decisions
- Build and maintain utilization and team-performance scorecards with automated alerting that surfaces trends in real time
- Analyze Support Economics, including cost-per-ticket modeling, self-service savings, and product quality costs
- Develop performance indexes and heatmaps identifying product areas and customers using the most support resources
- Lead dashboarding for AI adoption and return on investment, measuring business impact of AI-driven initiatives across Support Engineering
- Partner with go-to-market teams on predictive analytics and churn-prevention models to identify at-risk customers
- Define and refine unified customer-schema requirements to create consistent data across systems
- Deliver Monthly Business Review telemetry, executive-ready reporting packages, recurring operational reports, and ad hoc analysis
You will work in an all-remote, cross-functional environment partnering with Support Engineering, go-to-market teams, Business Operations, and Product Engineering across time zones.
Requirements:
- Experience as a business intelligence analyst, analytics engineer, or similar data role, ideally in a SaaS or technology company
- Experience partnering with Technical Support, Business Operations, Product Engineering, or similar organizations
- Fluency in SQL, including complex multi-table joins, grouping and aggregation, common table expressions, and conditional filters
- Hands-on experience building dashboards and reports in Zendesk Explore, Tableau, Looker, or similar business intelligence tools
- Ability to translate ambiguous business questions into structured analytical frameworks
- Strong presentation skills for both technical and non-technical stakeholders
- Ability to plan and prioritize work independently in a remote, cross-functional environment
- Nice-to-have: experience with large language model-based tools, AI/ML workflows, durable data solutions, analytics frameworks, or presenting to senior leaders and executives
