Vice President, Data Product Owner

BlackRock•Princeton, NY

2d•$140,000 - $215,000•Hybrid

About The Position

BlackRock’s Cross-Functional Technology (XFT) team brings together people, process, data, and technology to establish scalable, AI-enabled platforms and the technology foundation for the firm’s long-term growth strategy. XFT supports a unified, AI-first enterprise that simplifies decision-making and increases operational leverage. The Data Product Owner owns a portfolio of data products supporting BlackRock’s corporate functions. Working with business stakeholders, data and platform engineering, analytics teams, and enterprise data leadership, the role guides products from discovery and definition through delivery, adoption, and ongoing evolution. It combines hands-on product ownership with enterprise-data expertise: translating complex business needs into reusable, governed data products; shaping domain data models, semantic constructs, and enterprise definitions; and driving execution across stakeholder and technology teams. The role may direct a small group of analysts and other contributors while remaining actively involved in product definition, analysis, and delivery. For New York, NY, the stated salary range is USD $162,500–$215,000. For Princeton, NJ, the stated salary range is USD $140,000–$195,000. Employees are eligible for an annual discretionary bonus and benefits including healthcare, leave, and retirement benefits. BlackRock describes its compensation philosophy as pay-for-performance, with total compensation potentially varying by role, location, and firm, department, and individual performance. BlackRock offers a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents, and Flexible Time Off (FTO). The hybrid work model currently requires employees to work at least four days in the office per week, with the flexibility to work from home one day per week; some business groups may require more in-office time.

Requirements

At least 8 years of relevant experience in enterprise data, data products, product management, data architecture/modeling, data governance/stewardship, or related disciplines.
Demonstrated experience owning or leading data products across the product lifecycle.
Strong understanding of enterprise data concepts, including data definitions, data quality, lineage, metadata, ownership, and governance.
Hands-on experience with relational/normalized and dimensional data modeling, and ability to work effectively with business stakeholders, architects, and engineers.
Ability to operate independently and drive outcomes in a complex, matrixed enterprise environment.
Experience in one or more relevant corporate data domains, such as finance, treasury, tax, HR, client/revenue, legal and compliance, regulatory reporting, procurement, or enterprise master/reference data.

Nice To Haves

Deep experience in data product practice, including implementing or evolving product-development lifecycle processes, discovery, roadmap and backlog practices, prioritization, PI planning, and metrics.
Expertise in enterprise data modeling, including semantic/domain modeling, canonical concepts, master/reference data, normalization, dimensional models, and modeling standards.
Experience with Oracle Financials, SAP, TM1/Planning Analytics, Salesforce, Workday, Microsoft Dynamics, or Coupa.
Familiarity with cloud data platforms, BI/analytics platforms, metadata/catalog solutions, and data-quality tooling.
Experience in asset management, financial services, or a similar industry.

Responsibilities

Own assigned domain data products from discovery through roadmap, requirements, delivery, adoption, measurement, and lifecycle management.
Represent products through Agile planning and delivery, driving decisions, dependencies, prioritization, and execution to completion.
Lead product discovery and requirements analysis; evaluate business value, cost, dependencies, and competing priorities to recommend sequencing and tradeoffs.
Partner with business stakeholders to establish product outcomes, data definitions, service expectations, and measures of success.
Design and evolve domain data models, including core business concepts, relationships, reference and master data, and reusable data structures.
Partner with data engineering, platform, BI/reporting, and architecture teams on pipelines, transformations, access patterns, entitlements, analytical consumption, and distribution mechanisms.
Provide product direction and day-to-day leadership to small analyst teams.