HPE
Sr. Data Product Leader
$135K–$310K
Spring, Texas, United States of America +1 · Senior · Full-time · Posted Aug 27, 2026
- Location
- Spring, Texas, United States of America
Leixlip, Kildare, Ireland - Experience
- Typically 7+ years of related experience in product management, data management, or data strategy; preferably in financial services, leasing, or fintech.
- Education
- First-level university degree or equivalent experience; advanced university degree preferred in Business, Information Systems, Data Science, Computer Science, or a related field.
About the job
Owns the strategy, governance, lifecycle, and adoption of HPE Financial Services data products so they serve business reporting, analytics, automation, and AI use cases.
What you'll do
- Set the data product vision, roadmap, portfolio, and priorities; translate business and AI/analytics needs into product requirements.
- Manage data products through their lifecycle, establishing standards for quality, metadata, lineage, access, and responsible use.
- Coordinate business, technology, analytics, and governance stakeholders; track adoption, quality, and business value.
What they ask for
- Prior people management experience, including leading direct reports, matrixed teams, or cross-functional squads.
- Demonstrated experience treating data as a product, including defining roadmaps, managing backlogs, and measuring outcomes.
- Strong understanding of data governance, data quality, metadata, lineage, and data catalog concepts; ability to define AI-ready data requirements.
- SQL proficiency; experience working across global business, IT, data, analytics, and governance teams.
Nice to have
- Advanced university degree preferred; domain knowledge in leasing, asset management, or financial services operations preferred.
- Familiarity with modern data platforms and governance tools such as Databricks, Snowflake, Microsoft Fabric, or Collibra.
Pay
USD 135,000 - 310,500 base salary (annual; Texas)
Tools
SQL, Databricks, Snowflake, Microsoft Fabric, Collibra, Jira, Azure DevOps, D365
AI focus
Ensure data products are AI-ready and support AI-enabled experiences, analytics, and automation; define AI-readiness criteria and support responsible, traceable, auditable, risk-aligned data use.
Model-written summary · the original posting is authoritative