Sr Product Manager (Data)
We are looking for a Senior Product Manager to lead the product side of an Enterprise Context Layer for a US based company: the shared layer of agreed definitions, resolved entities, governed knowledge and access rules that their AI agents, customer-facing products and finance automations will draw on. You will decide what gets built and why, get business units to agree on what their data means, and challenge the architects and engineers who build it while driving internal adoption
What you'll do
Lead discovery with business and technical stakeholders: run working sessions, surface how data is used today, and turn what you learn into a prioritized set of use cases and requirements.
Own definitions and ownership: identify where metrics and business terms are inconsistent across teams and systems, and drive them to agreed, documented definitions with a named accountable owner for each.
Lead adoption across the business: get the teams that publish data to own its definitions, metrics and quality, and get the teams that consume context to draw from the shared layer rather than building their own version. Secure sponsorship and alignment at the leadership level when teams disagree.
Run a hypothesis-driven process: capture the client's assumptions, assign each to the session or experiment that validates it, and close each with a clear verdict and rationale.
Shape each increment with the Solution Architect: define scope, acceptance criteria and the order of vertical slices based on business value and risk.
Challenge technical decisions from a business perspective: ask whether a data model, a definition or a trade-off serves the people who will depend on it, and push back when it does not.
Keep a clear cadence with the Project Manager: decisions, open questions, risks and stakeholder commitments are tracked and visible.
Transfer ownership: leave the client with the processes, documentation and ownership model to keep the layer accurate after we step back.
What we're looking for (required)
5+ years in product management, with experience on data products (analytics, data platforms, metrics or semantic layers, internal data tools or similar).
Experience aligning stakeholders across business areas on shared definitions, priorities and ways of working.
A structured, evidence-based approach to discovery and prioritization.
Comfortable making decisions with incomplete information and managing senior stakeholders.
A record of getting products adopted, not just delivered.
Experience in defining, designing and delivering AI or LLM-based products.
Fluent written and spoken English.
Strong plus
Enough technical depth to challenge data architects and engineers on technical definitions, modeling choices and trade-offs, and to understand their answers.
Familiarity with modern data stacks (for example Databricks, Snowflake, dbt, data catalogs or governance tooling).