Data Product Analyst

Key Responsibilities

• 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 products 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.

Required Outputs / Artifacts

• Data Product Specification: scope, entities, grain, metrics, assumptions, transformation/derivation logic
• Data Product Contract: schema, SLAs, quality thresholds, lineage expectations, compatibility/versioning rules
• Release Readiness Note: consumer communication, backward compatibility, cutover plan, monitoring expectations