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 IntegriChains 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 asHIN 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.

Qualifications :

• 7 years of experience in Product Management Product Ownership Business Analysis or Data Product Management preferably working with enterprise data products or platforms.
• Demonstrated ownership of product roadmaps requirements backlogs prioritization and delivery of complex technology products.
• Experience working with one or more areas such as:
• Enterprise data platforms
• Data integration / ETL / ELT
• Data warehousing
• Data quality
• Data governance
• Reference or master data
• Analytics / reporting data products
• Metadata or semantic layers
• Ability to translate business problems into clear data and technical requirements without needing to be the engineer implementing the solution.
• Experience partnering effectively with Data Engineering Software Engineering QA Analytics and business/domain teams.
• Strong understanding of how data moves from source systems through transformation and quality processes into operational reporting analytics or AI applications.
• Proven ability to work in Agile/Scrum environments and manage product delivery using tools such as JIRA and Confluence.
• Strong analytical problem-solving documentation and stakeholder-management skills.
• Excellent written and verbal communication skills with the ability to communicate effectively with both business and technical audiences.

Preferred Qualifications

• Experience with Life Sciences healthcare pharmaceutical commercial data Market Access or Net Revenue .
• Familiarity with healthcare organization/customer data and industry identifiers such as HIN DEA NPI NCPDP 340B 844 852 or 867 .
• Experience with Snowflake dbt modern cloud data platforms data warehouses semantic models or analytics data products .
• Exposure to Master Data Management or reference data solutions such as Reltio Informatica MDM Veeva Network OneKey or similar platforms .
• Familiarity with concepts such as entity resolution matching hierarchies relationships survivorship and data stewardship.
• Experience supporting governed data assets used by analytics machine learning or generative AI applications .
• Experience working with distributed onshore/offshore engineering teams.

What Success Looks Like

• A clear product strategy and roadmap exists for enterprise data management and data products.
• Business problems are translated into scalable reusable data capabilities rather than one-off solutions.
• Engineering Data Science QA domain and business teams are aligned on priorities requirements dependencies and release timelines.
• Data from multiple internal and external sources can be consistently integrated standardized validated governed and consumed.
• Critical reference and master data capabilities are delivered where needed without making MDM the center of every data solution.
• Analytics reporting operational and AI use cases are supported by high-quality well-defined business-ready data products.
• Releases have clear acceptance criteria UAT validation documentation and operational handoff.

Additional Information :

What does IntegriChain have to offer

• Mission driven: Work with the purpose of helping to improve patients lives! 
• Excellent and affordable medical benefits non-medical perks including Flexible Paid Time Off and much more!
• Robust Learning & Development opportunities including over 700 development courses free to all employees

IntegriChain is committed to equal treatment and opportunity in all aspects of recruitment selection and employment without regard to race color religion national origin ethnicity age sex marital status physical or mental disability gender identity sexual orientation veteran or military status or any other category protected under the law. IntegriChain is an equal opportunity employer; committed to creating a community of inclusion and an environment free from discrimination harassment and retaliation.

Our policy on visa sponsorship for US based positions: Applicants for employment in the US must have valid work authorization that does not now and/or will not in the future require sponsorship of a visa for employment authorization in the US by IntegriChain.

Remote Work :

No

Employment Type :

Full-time