Senior Product Manager - Data Integrations

An overview of this role

As a Senior Product Manager for Data Integrations, you'll help GitLab's analytics and artificial intelligence (AI) agents see beyond the data held in GitLab. You'll decide which external systems connect to the Data Insights Platform and Orbit, which integrations come first, and how their data fits into shared models.

Engineering teams plan work, track incidents, monitor production, and manage security findings across different tools. Your challenge is to bring that context together with GitLab's code, merge requests, pipelines, deployments, and vulnerabilities. You'll shape a consistent approach that gives customers a trustworthy picture they can query, report on, and use with agents, rather than another set of disconnected data sources.

This role is based in Bangalore, India. You'll work from home and come to the Bangalore office when needed for business or team activities. You should be based in Bangalore or willing to relocate there.

• Set integration priorities using evidence from customers, field teams, and other product managers to identify the biggest gaps in context.
• Shape how external data maps into GitLab's shared data models, including the extract, transform, and load (ETL) approach and the transformations needed to make sources work together.
• Define the customer problems and user groups each integration serves. Partner with engineering to agree on the intended experience, trade-offs, and delivery sequence.
• Translate product decisions into a clear backlog of epics and issues for engineering.
• Define telemetry and adoption signals to understand whether connected data helps customers answer new questions, and use those findings to guide priorities.
• Help shape integration pricing and packaging as the product area matures.
• Use AI coding agents and GitLab to explore data, prototype integrations, and document the intended experience.
• Product management experience, with the ability to connect customer needs to priorities and make trade-offs with incomplete information.
• Strong knowledge of the software development and operations (DevOps) ecosystem, including how planning, security, observability, and IT service management tools fit into engineering workflows.
• A working understanding of data integration, ETL, and data transformation, including how data from different systems maps to a common model. You don't need a background as a data engineer.
• Comfort using AI tools for prototyping and documentation as part of your product work.
• Clear communication about customer problems, close collaboration with engineering, and a habit of using data to evaluate adoption.
• Experience with integration products, data models, or value stream analytics is helpful, but isn't required.

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