> Markdown version of [/jobs/ext/2220094-pxt-vp-data-owner-modeling-lead](https://www.wearedevelopers.com/jobs/ext/2220094-pxt-vp-data-owner-modeling-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # PXT VP Data Owner - Modeling Lead - **Company:** JPMorgan Chase & Co. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Data Architecture, Data Discovery, Data Governance, Database Queries, Programming Tools, Entity Relationship Models, Logical Data Models, Metadata, Meta-Data Management, Reference Data, Er-Win, Data Strategy, Databricks - **Published:** August 25, 2026 - **Apply:** https://www.jobmonkeyjobs.com/career/27960702/Pxt-Vp-Data-Owner-Modeling-Lead-New-Jersey-Jersey-City-7463 ## About the Role * 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. ## Description * 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 (e.g., customer, account, transaction) 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. 2) Domain Coverage & Governance * 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). 3) Stakeholder Partnership & Data Sourcing Strategy * 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. 4) Use Case Development & Business Alignment * 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. ## Related Videos - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Bringing Clarity to Event Streams: Enabling Analytics and AI Through Rich Metadata](https://www.wearedevelopers.com/videos/1616-bringing-clarity-to-event-streams-enabling-analytics-and-ai-through-rich-metadata) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) - [Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization](https://www.wearedevelopers.com/videos/253-data-fabric-in-action-how-to-enhance-a-stock-trading-app-with-ml-and-data-virtualization) ## Related Articles - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)