Job description

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. • 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. 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.

Requirements

Must-haves

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

Nice-to-haves

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.

Application prep

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Keywords to mirror

aiagilescrumjiramachine learning

Platform: smartrecruiters
Effort: medium

Benefits

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Paid Time Off