Sr. Data Product Leader

Hewlett Packard Enterprise•Spring, TX

11d•$135,000 - $310,500•Onsite

About The Position

Hewlett Packard Enterprise (HPE) is seeking a Sr. Data Product Leader to support their office. This role is designed as 'Onsite' and requires primary work from an HPE office. HPE Financial Services (HPEFS) is looking for a Data Product Leader to manage the daily execution of treating data as a managed, governed, and intentionally designed product within the HPEFS digital ecosystem. Reporting to the Digital Strategy Leader, this position is accountable for ensuring HPEFS data is trustworthy, governed, reusable, and AI-ready for reliable consumption across reporting, analytics, automation, AI-enabled experiences, and digital products. It is a hands-on, execution-focused role that acts as the main business-side representative for data consumers in Operations, Sales, Credit, Risk, Finance, Compliance, Analytics, and AI-enabled initiatives.

Requirements

First-level university degree or equivalent experience; advanced university degree preferred (Business, Information Systems, Data Science, Computer Science, or a related field).
Typically 7+ years of related experience in product management, data management, or data strategy, preferably within financial services, leasing, or fintech.
Prior people management experience required, including leading direct reports, matrixed teams, or cross-functional squads; ability to coach, develop talent, and drive accountability across geographies.
Demonstrated experience treating data as a product, including defining roadmaps, managing backlogs, and measuring outcomes.
Experience partnering across global teams spanning business, IT, data, analytics, and governance stakeholders.
Strong understanding of data governance, data quality, metadata, lineage, and data catalog concepts.
Ability to define AI-ready data product requirements, including semantic clarity, business context, appropriate-use guidance, and fitness for AI, analytics, and reporting consumption.
Understanding of responsible AI principles as they relate to governance, privacy, explainability, auditability, and risk management.
SQL proficiency and working familiarity with modern data platforms (e.g., Databricks, Snowflake, Microsoft Fabric), BI tools, and data catalog/governance tools (e.g., Collibra).
Familiarity with AI/ML data consumption patterns, semantic layers, feature stores, vector databases, data APIs, or model-ready datasets.
Working knowledge of model risk, data bias, data drift, and explainability concepts.
Experience defining common business metrics, semantic models, or reusable analytical datasets across functions.
Working knowledge of regulatory frameworks relevant to financial services (AML/KYC, SOX, GDPR, CCPA).
Experience working within Agile/Scrum delivery frameworks and tools such as Jira or Azure DevOps.
People leadership skills: coaching, performance management, team development, and driving execution through others.
Excellent written and verbal communication skills, with the ability to translate complex data topics into clear business language and executive-ready narratives.

Nice To Haves

Domain knowledge in leasing, asset management, or financial services operations preferred.

Responsibilities

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.
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.
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.
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.
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.
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.