Sr. Data Product Leader

Sr. Data Product Leader    This role has been designed as ‘’Onsite’ with an expectation that you will primarily work from an HPE office.

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE.

We are seeking a Sr. Data Product Leader to support our office.

HPE Financial Services (HPEFS) is seeking a Data Product Leader to own the day-to-day execution of treating data as a managed, governed, and intentionally designed product across the HPEFS digital ecosystem. Reporting to the Digital Strategy Leader, this role is responsible for ensuring HPEFS data is trusted, governed, reusable, and AI-ready so that it can be consumed reliably across reporting, analytics, automation, AI-enabled experiences, and digital products. This is a hands-on, execution-focused role an d serves as the primary business-side voice for data consumers across Operations, Sales, Credit, Risk, Finance, Compliance, Analytics, and AI-enabled initiatives.

Data Product Strategy & Roadmap   Own the data product vision, strategy, and roadmap for HPEFS, aligned to enterprise data-as-a-product direction and broader digital strategy.   Define and maintain the enterprise data product portfolio across key business domains, including Customer, Asset, Transaction, Risk, and Operational data.   Translate business needs, AI/analytics use cases, and reporting requirements into outcome-based data product requirements using the enterprise Outcome-Based Requirements (OBR) framework.   Prioritize the portfolio based on business value potential, reuse, risk reduction, and enablement of downstream analytics, automation, and AI use cases.   Align data product priorities with D365, Portals & APIs, Odessa, GPO, Pyramid, and other digital ecosystem initiatives, and continuously reassess the portfolio for new products, enhancements, consolidation, or deprecation.   Data Product Lifecycle Management   Own the full data product lifecycle from ideation and design through development, deployment, adoption, iteration, and deprecation.   Manage a prioritized backlog with clear acceptance criteria, business outcomes, OKR alignment, and release readiness expectations aligned to enterprise release governance.   Define and enforce product standards for quality, SLAs, metadata, lineage, cataloging, access controls, and usage guidance.   Ensure data products are reusable, composable, and scalable across consumption channels, including dashboards, APIs, semantic layers, governed datasets, analytical models, and AI-enabled solutions.   AI & Analytics Enablement   Ensure HPEFS data products are intentionally designed to support AI, advanced analytics, operational reporting, executive dashboards, automation, and digital product consumption.   Define AI-readiness criteria that go beyond baseline data product standards, including semantic clarity, business context, explainability, appropriate-use guidance, and fitness for machine consumption.   Ensure consumers understand intended use, known limitations, interpretation guidance, and downstream dependencies for each data product.   Translate AI, analytics, and automation needs into practical data product requirements in partnership with business, data science, reporting, and automation teams.   Support responsible AI practices by