• Translate domain needs across underwriting, pricing, claims, finance, and distribution into data product specifications with clear scope, assumptions, and acceptance criteria. • Define and maintain core insurance entities and relationships (e.g., policy, coverage, risk, claim, exposure, transaction), including grain and aggregation rules (e.g., policy-term, risk-level, claim-occurrence, transaction-level). • Define standard insurance metrics and logic (e.g., written/earned premium, loss ratio, frequency, severity, reserves) and ensure consistent interpretation across consuming teams. • Ensure the data product model accurately represents complex structures such as multi-line policies, endorsements, reinsurance structures, claims development, and reserving movements. • Design, enhance, and maintain data products in collaboration with data engineering and platform teams. • Oversee the full data product lifecycle, including versioning, enhancements, deprecation, and backward compatibility. • Support domain prioritization and roadmap planning for data products; maintain a transparent backlog aligned to business outcomes and OKRs. • Define and maintain the documentation to ensure certified data product are shipped with appropriate metadata including business definitions, lineage, quality expectations, usage guidance, ownership. • Collaborate with Data Quality and Data Governance teams to apply federated standards while preserving domain ownership. • Define and execute validation and testing strategies (quality thresholds, completeness checks, reconciliation validations) to ensure reliability for analytics and reporting consumers. • Perform impact analysis and coordinate change management for schema/metric/logic changes across consuming domains; communicate changes clearly and manage transition plans. • Act as the primary point of contact for data product consumers - supporting adoption, correct usage, interpretation, and self-service enablement. • Contribute to reusable templates/playbooks (metric definition template, acceptance criteria checklist, “definition of done” for data products) and coach domain SMEs in applying them consistently.