Lead Data Product Manager – Pharmacy Data Products

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The Lead Data Product Manager owns the lifecycle of Center Well 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.

• 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.

• 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 semantics
  
  Documented lineage and dependencies
  
  Versioning and change management expectations
  
  Validation and reconciliation criteria for cutover

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 rules
  
  Set thresholds/SLAs and issue management workflow
  
  Track and reduce recurring defects

• Define dataset lifecycle practices: retention, deprecation, and controlled evolution over time.

• Improve accessibility and usability for analysts and associates by standardizing:
  Definitions, naming conventions, documentation, and examples
  
  Access 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.

• 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).

• Operate within an Agile SAFe delivery model:
  Contribute to PI Planning, refinement, and ART ceremonies
  
  Write and manage Features (SAFe) and User Stories with clear acceptance criteria
  
  Maintain 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).

• Bachelor's Degree

• 6–10+ years in product management, data…