Senior Product Manager, Data Products

Role overview Define the data products and shared metrics that help organizations understand product value, adoption, and activity. The role begins with data foundations—deciding what metrics mean and what reliable instrumentation requires—then carries those definitions into internal analytics, customer reporting, APIs, and AI-assisted analysis.

Responsibilities - Set the direction for data products, evolving reporting toward insights that help organizations decide what to do next. - Define shared metrics across product areas and standards that teams can use to publish measures consistently. - Help product managers explore analytics and establish meaningful success metrics, coordinating with business analytics. - Own the strategy and roadmap for customer reporting, including dashboards, exports, APIs, audit logs, and the methods behind reported figures. - Shape a customer-facing reporting MCP server, including its data access, availability, and guardrails; guide natural-language and agent-driven analysis. - Set requirements for event detail and retention, and work with data engineering and product teams to improve instrumentation.

Requirements - At least five years of product management experience, including shipping an analytics or reporting product for paying business customers and defining its metrics. - A record of making inconsistent data trustworthy through metric definitions, implementation-ready specifications, and upstream instrumentation changes. - Experience with both internal or developer-facing platform products and customer-facing analytics or reporting products. - Working knowledge of event pipelines, data warehouses or lakehouses, dbt-style modeling, and semantic layers; ability to write SQL and investigate data directly. - Ability to align teams across product, data engineering, analytics, finance, and go-to-market without direct authority.

Nice to have - Experience with enterprise administration or governance products, such as identity, security, compliance, or audit tools. - Hands-on experience with semantic-layer products such as dbt Semantic Layer, Cube, or LookML. - Experience designing analytics APIs, MCP servers, or data connectors.