Senior Data Product Leader

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 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.

Knowledge and Skills

• 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.

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

• 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.

Additional Skills

Accountability, Active Listening, Agile Methodology, Business Acumen, Change Management, Coaching, Cross-Functional Collaboration, Data Governance, Data Product Management, Data Quality, Decision Making, Executive Communication, Growth Mindset, Influencing Skills, Leadership, Managing Ambiguity, Outcome-Based Requirements, People Management, Product Management, Responsible AI, Roadmap Planning, Stakeholder Engagement, Strategic Thinking, Talent Development.