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The Lead Data Product Owner supports the lifecycle of CenterWell Pharmacy (CWP) and CenterWell Specialty Pharmacy (CWSP) data products, ensuring data assets are trusted, governed, discoverable, and reusable across analytics, operational workflows, digital experiences (web/mobile), and emerging AI-enabled self-service.
This role translates CWP and CWSP business needs into high-quality user stories and a well-managed backlog , enabling engineering teams to deliver reliable, productized data-particularly as legacy pharmacy datasets are modernized and migrated to Databricks .
The role partners closely with the Lead Data Product Manager - CWP/CWSP , who owns product strategy and outcomes. The Lead Data Product Owner is primarily execution-focused , responsible for story definition, backlog management, delivery alignment, and ensuring existing governance and stewardship models are consistently applied and aligned with enterprise data policy within an Agile SAFe environment.
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The Lead Data Product Owner is accountable for ensuring CenterWell Pharmacy data-across mail-order, retail, and specialty fulfillment-reaches consumers as well-defined, governed, and reusable data products. As CWP/CWSP modernizes legacy pharmacy data to Databricks, this role makes sure those datasets are not just moved , but productized : documented, quality-managed, and ready to power analytics, operational workflows, and member-facing digital experiences.
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.
Key Responsibilities
1) Own the CWP & CWSP Data Product Backlog (New + Legacy)
Partner with the Lead Data Product Manager - CWP/CWSP to translate pharmacy strategy into a prioritized, story-driven backlog .
Maintain an inventory of CWP and CWSP data products (e.g., prescription/refill journey, fulfillment milestones, operational status, specialty patient journey, clinical interventions, member experience signals).
Identify high-value legacy datasets-especially those critical to specialty pharmacy operations and member-facing digital experiences-that require clarification, remediation, or protection during modernization.
Ensure backlog prioritization drives measurable outcomes such as improved self-service adoption, reduced time-to-insight, fewer data defects, and stronger downstream product enablement.
2) User Story Leadership (Primary Execution Responsibility)
Write clear, testable user stories that reflect CWP and CWSP consumer needs (analytics, operational, reporting, digital web/mobile).
Decompose complex cross-entity requirements into incremental stories (e.g., curated fulfillment datasets, specialty-specific views, governed extracts for digital experiences).
Define acceptance criteria that reinforce:
Correct business semantics
Data quality expectations
Documentation completeness
Readiness for consumption
3) Productize Data During Modernization / Migration to Databricks
Ensure CWP and CWSP data modernization efforts result in managed data products , not unmanaged technical artifacts.
Partner with engineering and architecture to ensure migrated datasets meet established standards for:
Business definitions and consistent semantics
Lineage and dependency visibility
Versioning and change management
Reconciliation and validation for cutover (especially fulfillment, clinical, and financial data)
4) Sustain and Operationalize Existing Data Governance & Stewardship
(Apply and reinforce existing models-do not create net-new frameworks.)
Apply and reinforce existing governance and stewardship models across CWP and CWSP data products.
Ensure datasets consistently align with enterprise data policy , standards, and decision rights.
Partner with data governance, stewardship, and domain leaders to:
Maintain clear ownership and accountability for CWP and CWSP datasets
Ensure required metadata, glossary alignment, and documentation remain current
Identify gaps where governance standards are not consistently applied and convert them into backlog items
Support ongoing data quality management by:
Translating existing CDEs and quality rules into actionable delivery stories
Tracking recurring defects and ensuring fixes are prioritized and durable
Ensure dataset lifecycle practices (retention, deprecation, evolution) are followed in alignment with corporate policy and any applicable specialty pharmacy regulatory requirements.
5) Enable Self-Service Analytics & AI Readiness Through Consistency
Improve usability and trust by ensuring CWP and CWSP data products consistently meet established standards for:
Definitions and naming conventions
Documentation, examples, and common query patterns
Secure access and entitlement patterns
Ensure data products are reusable across teams and use cases-supporting future AI self-service layers by strengthening consistency, labeling/semantics, and discoverability.
6) Support Downstream Product Experiences (Web/Mobile & Operational) via Data Contracts
Partner with digital and operational product teams to expose data appropriately-e.g., reliable data products representing "where a refill is in its journey" or "specialty patient onboarding status" for web/mobile experiences.
Define and maintain data contracts with consumers that clarify:
Availability and freshness expectations
Schema evolution and change notification
CWP vs. CWSP consumer responsibilities where datasets differ
7) Deliver Through SAFe Agile with Strong Backlog Discipline
Operate within SAFe with a story-first mindset :
Participate in PI Planning, refinement, and ART ceremonies
Ensure backlog readiness and dependency clarity
Write and manage Features and User Stories with clear acceptance criteria
Coordinate across engineering, analytics, platform, security/compliance, and CWP/CWSP business stakeholders.
Track delivery and product health metrics such as adoption, quality incidents, freshness SLAs, completeness, and consumer satisfaction.
Use your skills to make an impact
Required Qualifications
Bachelor's degree (or equivalent experience)
6-10+ years in data product ownership, analytics delivery, analytics product ownership, or related roles
Strong ability to write high-quality user stories and acceptance criteria for data products
Comfortable performing SQL-based data exploration/validation (not expected to code pipelines, but able to query data to support requirements and problem-solving)
Experience working within established governance frameworks and aligning delivery to enterprise standards
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)
Experience supporting pharmacy fulfillment, specialty pharmacy, or member-facing digital products
Familiarity with DAMA-DMBOK2 concepts applied pragmatically (governance, metadata management, data quality management, stewardship)
Healthcare, pharmacy, claims, or regulated data environment experience
Demonstrated experience productizing data assets: improving governance, metadata, usability, quality, and adoption-not just moving data
Proven ability to operationalize governance through delivery, not process creation
Proven ability to work effectively in SAFe / Agile environme
