Senior/Group Product Manager, AI Ecosystem & Semantic Layer

About the Role

AI only answers correctly when it can trust the data underneath it. This role owns two connected parts of how Sigma shows up in that world: where Sigma's experience lives outside its own product (MCP, a CLI, the Claude and ChatGPT marketplaces, integrations like Slack, Teams, and Glean), and the semantic layer that makes every one of those surfaces trustworthy.

Mandates

• AI Ecosystem: Defining Sigma's approach to MCP, giving external agents structured, governed access to Sigma's data model; owning the CLI, giving developers a fast way to work with Sigma outside the UI; and building Sigma's presence in the Claude and ChatGPT marketplaces, plus integrations for Slack, Teams, Glean, and similar surfaces.
• Semantic Layer: Setting the roadmap for how semantic views connect across data platforms and how the model evolves as new AI capabilities emerge to ensure reliability and consistency across all agent and chat surfaces.

What You'll Do

• Define Sigma's approach to MCP, giving external agents and tools structured, governed access to Sigma's data model.
• Own the CLI roadmap, giving developers a fast, scriptable way to work with Sigma outside the UI.
• Build Sigma's presence in the Claude and ChatGPT marketplaces, along with integrations for Slack, Teams, Glean, and similar surfaces.
• Set the roadmap for Sigma's data modeling strategy, including how semantic views connect across data platforms and how the semantic layer evolves as new AI capabilities emerge.
• Partner with engineering and design to ship integration and semantic modeling capabilities that hold up at enterprise scale.

What We're Looking For

• 5+ years of product management experience in B2B SaaS, data platforms, analytics, or developer-facing products.
• Familiarity with MCP, APIs, or other machine-to-machine interfaces, and how agents consume structured data.
• Experience building or launching integrations with platforms like Slack, Microsoft Teams, or enterprise search tools such as Glean.
• Comfortable defining a CLI or other developer-facing product from the ground up.
• Strong understanding of data modeling concepts (semantic layers, metrics layers, dimensional modeling) and how they connect across a modern data stack.
• Some background in BI or analytics is a plus.

Additional Details

• Work environment: In-office work environment in San Francisco with an expected 25% travel requirement.