Product Manager - Data Management & Data Products

We are looking for an experienced Product Manager to help define and deliver enterprise data management and data product capabilities supporting IntegriChain's Life Sciences solutions.

You will own the roadmap and requirements for capabilities that ingest, standardize, integrate, govern, enrich, and deliver high-quality data across our platform.

The ideal candidate has experience building or managing enterprise data platforms, data products, data integration, data quality, data governance, reference/master data, or analytics-ready data assets.

You should be comfortable understanding complex business and data problems, working with technical teams to design scalable solutions, and translating those needs into product strategy, epics, features, user stories, and measurable outcomes.

Experience with Life Sciences, healthcare, Net Revenue, or commercial data is valuable, but we are looking first for a strong data-oriented Product Manager who can learn the domain and drive product direction.

What This Role Entails

• Product Strategy & Roadmap: Own and evolve the product roadmap for enterprise data management and data products, aligning investments with customer needs, product strategy, and business objectives.

• Product Discovery: Partner with customers, domain SMEs, engineering teams, and internal stakeholders to identify data challenges and validate problems before defining solutions.

• Data Product Management: Define capabilities for acquiring, integrating, standardizing, enriching, governing, and publishing data for operational, analytics, AI, and reporting use cases.

• Requirements & User Stories: Translate business and data problems into epics, features, user stories, acceptance criteria, process flows, and data requirements that engineering teams can confidently implement.

• Data Integration: Partner with Data Engineering on ingestion, ETL/ELT, source-to-target mappings, transformations, validation, reconciliation, and downstream data delivery.

• Data Quality & Governance: Define product requirements around data quality, validation, lineage, metadata, reference data, stewardship, monitoring, and governance.

• Reference & Master Data: Support capabilities for managing critical business entities such as healthcare organizations, customers, outlets, products, and other reference/master data. This may include identity resolution, matching, hierarchies, relationships, and mastered records where appropriate.

• Analytics & AI-Ready Data: Enable reusable, well-defined data assets, semantic models, and governed data products that support reporting, analytics, and AI applications.

• Net Revenue & Life Sciences Data: Work with domain teams to understand data requirements across areas such as Chargebacks, Government Pricing, contracts, claims, sales, customer, and reference data.

• Industry Data Assets: Support productization and integration of Life Sciences datasets and identifiers such as 844, 867, 852, HIN, DEA, NPI, NCPDP, and 340B.

• Prioritization: Facilitate prioritization across stakeholders using business value, customer impact, technical dependencies, data quality, risk, and strategic alignment.

• Agile Delivery: Partner with Engineering and QA through backlog refinement, sprint planning, estimation, testing, and delivery while proactively managing risks and dependencies.

• QA / UAT: Define acceptance criteria and test scenarios, coordinate UAT, and ensure traceability between requirements and delivered capabilities.

• Stakeholder Management: Lead workshops and working sessions across Product, Engineering, Data Science, Analytics, Operations, customer-facing teams, and leadership.

• Release Management: Drive release readiness, documentation, rollout communication, adoption, and post-release feedback.