PXT VP Data Owner - Modeling Lead

JPMorgan Chase & Co.•Jersey City, NJ

6h•$147,250 - $215,000

About The Position

This role is responsible for data modeling and structural integrity, ensuring entities, attributes, and models are well-structured and consistently defined across domains. The position involves mapping cross-product dependencies, identifying architecture gaps, redundancies, and misalignments, and ensuring semantic consistency for shared concepts. The lead will partner with data engineers and modelers to enforce standards for data quality, cleanliness, normalization, and maintainability. Additionally, the role involves maintaining a comprehensive view of active data domains, driving domain completeness reviews, and establishing data product classifications. A key aspect is stakeholder partnership, developing data sourcing strategies, facilitating discovery sessions, and translating technical concepts into business language. The role also focuses on use case development, applying a user-first approach to develop use cases tied to business problems, conducting discovery interviews, building a use-case repository, and collaborating with analytics and product teams to validate business outcomes.

Requirements

5–8 years in data strategy, data architecture, data governance, or data product management within a complex enterprise environment.
Demonstrated ability to read, interpret, and critique conceptual and logical data models; strong familiarity with ER modeling and metadata frameworks.
Strong understanding of data governance principles: domains, ownership, stewardship, master/reference data, lineage, and controls.
Proven track record translating business requirements, data product specifications.
Experience working across multiple lines of business and domains in a matrixed environment.
Familiarity with data cataloging/modeling and developer tools (e.g., Unity, AWS, Erwin, Databricks).
SQL proficiency is a plus.

Responsibilities

Audit and govern relationships between data products to ensure entities, attributes, and models are well-structured and consistently defined across domains.
Map cross-product dependencies and identify architecture gaps, redundancies, and misalignments.
Ensure semantic consistency so shared concepts have consistent meaning, definitions, and lineage across domains.
Partner with data engineers and data modelers to enforce standards for data quality, cleanliness, normalization, and maintainability.
Maintain a comprehensive view of all active data domains, ensuring no domain is orphaned or underrepresented in the product taxonomy.
Drive domain completeness reviews, identifying missing data products and surfacing gaps to leadership and domain owners.
Establish and maintain data product classifications (e.g., authoritative/golden vs. derived, curated vs. raw, operational vs. analytical).
Partner with business stakeholders, data owners, and domain leads to ensure data lands in the right schemas for the right purposes.
Develop and maintain data sourcing strategies: identify authoritative sources, resolve source conflicts, and plan for future acquisition/coverage.
Facilitate data discovery sessions and working groups to validate product scope, align definitions, and close sourcing gaps.
Translate complex technical concepts into clear business language and drive decisions to resolution.
Apply a user-first approach to develop use cases tied to concrete business problems, translating stakeholder needs into actionable data product requirements.
Conduct discovery interviews with business consumers to surface unmet needs and validate whether existing products are sufficient.
Build and maintain a use-case repository aligned to strategic priorities to support roadmap planning and prioritization.
Collaborate with analytics and product teams to validate that delivered data products drive measurable business outcomes.