Lead Data Product Manager - Pharmacy Data Products

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The Lead Data Product Manager owns the lifecycle of CenterWell Pharmacy data products—ensuring data assets are trusted, governed, discoverable, and reusable across analytics, operational workflows, digital experiences (web/mobile), and future AI-enabled self-service. This role is accountable for turning both new and legacy pharmacy datasets into well-defined, productized data products, particularly as data is modernized and migrated to Databricks.

This position requires an individual who can operate at the strategy level when needed, but is primarily detail-oriented and execution-focused—able to define requirements, manage backlogs, write SAFe features and user stories, align stakeholders, and drive delivery with engineering teams in an Agile SAFe environment.

Key Responsibilities

1) Own the Pharmacy Data Product Portfolio (New + Legacy)

• Define and manage a portfolio of pharmacy domain data products (e.g., prescription/refill journey , fulfillment milestones, operational status, member experience signals).
• Identify and prioritize high-value legacy tables and datasets that have not been "productized" and are at risk during modernization.
• Establish product roadmaps tied to measurable outcomes: improved self-service adoption, reduced time-to-insight, fewer data defects, and stronger downstream product enablement.

2) Productize Data During Modernization / Migration to Databricks

• Lead product definition for data modernization efforts, ensuring legacy tables become managed data products, not unmanaged technical artifacts.
• Partner with engineering and architecture to ensure migrated datasets include:Clear business definitions and consistent semanticsDocumented lineage and dependenciesVersioning and change management expectationsValidation and reconciliation criteria for cutover

3) Implement Practical Data Governance Foundations (DAMA-aligned)

In collaboration with domain leaders, engineering, and data governance partners:

• Establish ownership and stewardship for important datasets (who owns, who approves changes, who resolves issues).
• Create metadata and documentation standards(business glossary, dataset descriptions, field definitions, usage guidance).
• Operationalize data quality management:Define critical data elements (CDEs) and quality rulesSet thresholds/SLAs and issue management workflowTrack and reduce recurring defects
• Define dataset lifecycle practices: retention, deprecation, and controlled evolution over time.

4) Enable Self-Service Analytics and AI Readiness Through Better Usability

• Improve accessibility and usability for analysts and associates by standardizing:Definitions, naming conventions, documentation, and examplesAccess patterns and secure sharing/entitlements"How to use" guidance and common query patterns
• Ensure data products are designed for reuse across teams and use cases—supporting future AI self-service layers by strengthening consistency, labeling/semantics, and discoverability.

5) Support Downstream Product Experiences (Web/Mobile & Operational)

• Partner with digital and operational product teams to expose data appropriately. This exposure is achieved through product capabilities, such as convenient, reliable data products that represent "where a refill is in its journey" for web/mobile experiences.
• Define and maintain data contracts and consumer expectations (availability, freshness, definitions, and schema evolution).

6) Deliver via SAFe Agile (Features, Stories, and Backlog Ownership)

• Operate within an Agile SAFe delivery model:Contribute to PI Planning, refinement, and ART ceremoniesWrite and manage Features (SAFe) and User Stories with clear acceptance criteriaMaintain and prioritize a product backlog aligned to business outcomes and technical dependencies
• Coordinate cross-functionally across engineering, analytics, platform teams, compliance/security, and business stakeholders.
• Track delivery progress and product KPIs (adoption, quality incidents, freshness, completeness, and consumer satisfaction).

Use your skills to make an impact

Required Qualifications

• Bachelor's Degree
• 6–10+ years in product management, data product management, analytics product ownership, or a related role delivering data capabilities at scale.
• Comfortable performing basic data exploration/validation using SQL (not expected to code pipelines, but able to query data to support requirements and problem-solving).
• Strong stakeholder management: able to align domain SMEs, engineering, analytics, and leadership around shared definitions and priorities.

Preferred Qualifications

• Experience with Databricks / lakehouse modernization (or similar platform migrations).
• Familiarity with DAMA-DMBOK2 concepts applied pragmatically (governance, metadata management, data quality management, stewardship).
• Experience with healthcare/pharmacy/claims/fulfillment or regulated data environments.
• Demonstrated