Senior Data Product Manager
Baylor Scott & White Health· enterprise· Hospital & Health Care· Oct 22, 2026 · 6d ago
United States
Remote
Full-time
Senior
$129K–$184K est.
10+ years exp
Job description
JOB SUMMARY
The Senior Data Product Manager is accountable for ensuring assigned enterprise data domains are ready to power BSWH’s priority AI use cases. This role owns the quality, reliability, availability, usability, and lifecycle management of data products required by AI product, engineering, ontology, analytics, and operational teams.
This role partners closely with AI Product, Engineering, Data & Analytics, Knowledge/Ontology, Architecture, Security, Privacy, Operations, and domain subject matter experts to translate AI use-case needs into clear data product requirements, data contracts, quality expectations, latency requirements, remediation priorities, and operating commitments.
The Senior Data Product Manager ensures that data required for AI-enabled products is trusted, governed, discoverable, fit-for-purpose, and operationally sustainable. The role is a critical bridge between AI use-case teams and the data organization, helping ensure that data domains consistently meet current and future AI delivery needs.
ESSENTIAL FUNCTIONS OF THE ROLE
Data Domain Management & Product Strategy
· Manage specific set of data domains required to support BSWH’s AI use-case roadmap and related customer, operational, clinical, financial, or enterprise workflows.
· Define the product vision, roadmap, backlog, and success metrics for assigned AI-ready data products and domains.
· Translate AI use-case needs into clear data product requirements, including data elements, definitions, source systems, availability expectations, freshness requirements, lineage needs, and quality standards.
· Prioritize data work based on AI use-case value, delivery urgency, dependency risk, quality gaps, operational impact, and feasibility.
· Create a clear roadmap for improving assigned data domains so they can reliably support current and future AI-enabled products.
AI Use Case Enablement
· Partner with AI Product Managers, engineering teams, data teams, ontology teams, and operational stakeholders to identify the data required to power each assigned AI use case.
· Ensure required data elements are available, understood, governed, and fit for AI consumption before key delivery milestones.
· Clarify data dependencies, gaps, risks, and remediation actions required to enable AI use-case teams to deliver safely and effectively.
· Support translation of jobs-to-be-done, user stories, agent requirements, and product acceptance criteria into data product requirements.
· Ensure data products can support AI workflows that may require timely retrieval, reliable lookups, secure access, source attribution, and operational consistency.
Data Quality, Reliability & Remediation
· Define and manage data quality expectations for assigned domains, including completeness, accuracy, consistency, timeliness, availability, lineage, and usability.
· Identify and prioritize data quality gaps that create risk for AI use cases, customer experience, operational workflows, or decision support.
· Partner with data engineering, source system owners, analytics, governance, and operational teams to remediate data issues and improve domain reliability.
· Establish mechanisms to monitor data quality, data freshness, availability, and reliability across assigned domains.
· Ensure recurring data issues have clear ownership, escalation paths, root-cause analysis, and sustainable remediation plans.
Data Contracts, Access & Operational Commitments
· Define and maintain data contracts for assigned domains, including data definitions, ownership, access patterns, refresh expectations, latency requirements, lineage expectations, and downstream consumption needs.
· Partner with engineering and architecture teams to ensure data access patterns support AI use-case requirements, including APIs, data products, semantic models, retrieval layers, and governed consumption paths.
· Ensure data products are discoverable, documented, understandable, and usable by approved consumers across AI, product, engineering, analytics, and operational teams.
· Help ensure assigned data products meet agreed expectations for availability, freshness, performance, scalability, security, privacy, and operational support.
· Coordinate with governance and security stakeholders to ensure sensitive data is accessed and used appropriately.
Cross-Functional Prioritization & Stakeholder Alignment
· Serve as the primary product manager for assigned data domains, aligning stakeholders around data priorities, tradeoffs, dependencies, and delivery commitments.
· Facilitate prioritization across competing AI use-case needs, source system constraints, data engineering capacity, governance requirements, and operational priorities.
· Communicate roadmap priorities, data readiness status, risks, blockers, quality issues, and remediation progress to stakeholders and leadership.
· Partner with domain subject matter experts to ensure data definitions, business rules, and usage expectations reflect real operational and clinical context.
· Drive alignment across teams so that AI use-case delivery is not slowed by unclear data ownership, undefined requirements, or unresolved quality gaps.
Continuous Improvement & Product Lifecycle Management
· Manage assigned data products across their lifecycle, including discovery, requirements, delivery, adoption, monitoring, improvement, and retirement or replacement when appropriate.
· Use feedback from AI use-case teams, engineering teams, analytics teams, operations, and data consumers to continuously improve data product usability and reliability.
· Identify opportunities to standardize definitions, reduce duplication, improve cross-domain interoperability, and expand reuse of trusted data products.
· Partner with Knowledge/Ontology and semantic layer teams to ensure assigned data domains support consistent meaning, relationship mapping, and AI-ready context.
· Contribute to enterprise practices for AI-ready data product management, including playbooks, templates, metrics, standards, and governance processes.
KEY SUCCESS FACTORS
· Proven ability to own data products or data domains that support complex business, operational, analytics, or technology use cases.
· Strong understanding of data quality, data reliability, data availability, data lineage, data definitions, source systems, and operational data dependencies.
· Ability to translate ambiguous AI use-case needs into clear data requirements, data contracts, quality expectations, and prioritized delivery roadmaps.
· Strong product management skills, including prioritization, backlog management, stakeholder alignment, lifecycle management, and outcome measurement.
· Ability to partner effectively with data engineering, product, AI engineering, ontology, analytics, source system, governance, security, and operational stakeholders.
· Strong judgment in balancing business value, data quality risk, technical feasibility, latency requirements, security/privacy requirements, and delivery urgency.
· Experience improving data products or domains so they are trusted, discoverable, reusable, governed, and fit for production consumption.
· Comfort operating through influence in a matrixed environment where data ownership, quality remediation, and prioritization require cross-functional alignment.
· Strong analytical and problem-solving skills, with the ability to identify root causes of data issues and drive sustainable remediation.
· Comfort operating in regulated, high-trust, or mission-critical environments where data quality, privacy, reliability, and customer trust are essential.
About Us
Here at Baylor Scott & White Health we promote the well-being of all individuals, families, and communities. Baylor Scott and White is the largest not-for-profit healthcare system in Texas that empowers you to live well.
Our Core Values are:
• We serve faithfully by doing what's right with a joyful heart.
• We never settle by constantly striving for better.
• We are in it together by supporting one another and those we serve.
• We make an impact by taking initiative and delivering exceptional experience.
Benefits
Our benefits are designed to help you live well no matter where you are on your journey. For full details on coverage and eligibility, visit the Baylor Scott & White Benefits Hub to explore our offerings, which may include:
• Immediate eligibility for health and welfare benefits
• 401(k) savings plan with dollar-for-dollar match up to 5%
• Tuition Reimbursement
• PTO accrual beginning Day 1
Note: Benefits may vary based upon position type and/or level.
Belonging Statement
We believe that all people should feel welcomed, valued and supported.
MINIMUM REQUIREMENTS
Education
· Bachelor’s degree in Data Science, Information Systems, Computer Science, Engineering, Business, Healthcare Administration, Analytics, or related field.
· Master’s degree preferred.
Experience
· 10–15 years of experience in data product management, data ownership, analytics, data governance, data engineering, product management, or related technology/data roles.
· 5–10+ years of experience owning data products, data domains, analytics products, data platforms, or data-enabled business capabilities.
· Experience working with cross-functional teams to define data requirements, prioritize data work, resolve quality issues, and deliver reusable data products.
· Experience with data quality, data lineage, metadata, data definitions, source system analysis, data contracts, data access patterns, and production data reliability.
· Experience partnering with product, engineering, analytics, governance, security, privacy, operations, and business stakeholders to deliver complex data-enabled outcomes.
· Experience supporting AI, machine learning, advanced analytics, digital products, customer-facing workflows, or operational decision-support capabilities preferred.
· Healthcare, life sciences, financial services, or other regulated-industry experience strongly preferred.
Required Technical Expertise
· Strong understanding of enterprise data products, data domains, data quality practices, data governance, data lineage, and data lifecycle management.
· Familiarity with modern data platforms, APIs, data pipelines, semantic layers, data catalogs, data quality tooling, metadata management, and operational data consumption patterns.
· Ability to define data product requirements for availability, freshness, latency, refresh cadence, source attribution, access control, reliability, and downstream usage.
· Understanding of AI-ready data concepts, including trusted source data, semantic consistency, retrieval patterns, embeddings/vectorization concepts, knowledge/ontology dependencies, and governed consumption.
· Strong analytical skills and comfort working with data engineers, architects, analysts, source system owners, and operational stakeholders to investigate and resolve data issues.
· Strong understanding of privacy, security, compliance, and appropriate data use considerations for enterprise systems handling sensitive data.
PREFERRED QUALIFICATIONS
· Experience serving as a data product manager, data product owner, data domain owner, data steward, analytics product owner, or product manager for data-intensive capabilities.
· Experience enabling AI, machine learning, data science, digital product, or operational workflow teams through trusted data products.
· Experience defining or managing data contracts, service-level expectations, quality metrics, data readiness criteria, or production support expectations for data products.
· Experience with healthcare data domains such as patient, provider, location, scheduling, encounters, medications, claims, billing, clinical documentation, cost, operations, or customer engagement data.
· Experience partnering with ontology, semantic layer, knowledge graph, or metadata teams to improve data meaning, contextualization, and reuse.
· Experience working with data quality, cataloging, governance, master data management, API, cloud data platform, or analytics engineering tools.
· Experience leading through ambiguity, cross-functional prioritization, source-system complexity, and evolving AI or analytics requirements.
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