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.
Who We Are:
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.
Job Description:
We are seeking a Sr. Data Product Leader to support our office.
Job Description
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 and serves as the primary business-side voice for data consumers across Operations, Sales, Credit, Risk, Finance, Compliance, Analytics, and AI-enabled initiatives.
Responsibilities
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 ensuring data used for AI-enabled insights or decisions is traceable, auditable, and risk-aligned.
Identify opportunities where trusted data products unlock predictive insights, intelligent workflow automation, customer intelligence, risk visibility, and faster time-to-insight.
Data Governance & Quality
Serve as the business-side steward of data governance for assigned domains, ensuring adherence to enterprise policies and standards.
Own business glossary definitions, data dictionaries, sensitivity classification, and domain-level metadata for assigned data domains.
Define data quality rules, monitoring thresholds, and remediation paths, and drive root cause analysis for issues that impact reporting, AI outputs, or business decisions.
Ensure data products comply with regulatory requirements, including AML/KYC, SOX, GDPR, CCPA, and internal audit standards.
Partner with the HPE Data Office and IT on governance frameworks, tooling such as Collibra, and enterprise data catalog implementation.
Cross-Functional Collaboration & Stakeholder Engagement
Serve as the primary liaison between data consumers (Operations, Sales, Credit, Risk, Finance, Compliance) and data producers (IT, Data Engineering, Analytics, Data Science).
Facilitate domain working sessions to capture requirements, validate data product design, and drive alignment on priorities and tradeoffs.
Partner with Business Product Managers, Business Analysts, and Process Engineering so data products support end-to-end process and product outcomes.
Collaborate with Product Enablement to strengthen data and AI literacy, adoption, and responsible consumption across business teams.
Coordinate with the Product Insight/Analytics Lead on shared measurement, dashboards, and value realization reporting; engage external vendors as needed under HPEFS vendor governance.
Measurement, Adoption & Value Realization
Define and track KPIs for each data product, including adoption, data quality, consumer satisfaction, time-to-insight, reuse, and business value delivered.
Track outcomes tied to enterprise objectives, such as reduced manual reporting, faster insight generation, improved decision confidence, and stronger self-service adoption.
Communicate data product updates, roadmap progress, risks, and value realization to the Digital Strategy Leader and senior leadership.
Drive product-level continuous improvement through structured feedback loops, usage analytics, and periodic data product reviews.
Education and Experience Required
First-level university degr