PXT Data Owner - Modeling Lead - VP

JPMorganChase•Jersey City, NJ

39d

About The Position

Join a team where you can shape how enterprise data is defined, governed, and made usable at scale. In this role, you will influence decisions that improve trust in data products and accelerate business outcomes. You will work with partners across product, engineering, and business teams, building relationships and driving clarity. You will also have opportunities to grow your impact by leading cross-domain governance and architecture initiatives. Job Summary As a PXT Data Owner – Modeling Lead in the People Experience and Data Strategy Team , you will connect data modeling, data product design, engineering execution, and business needs to ensure data products are fit for use. You will help establish consistent definitions for shared concepts, strengthen lineage and discoverability, and ensure the right data lands in the right place for the right purpose. You will drive structural integrity across domains by identifying gaps, redundancies, and misalignments. You will partner across functions to turn ambiguity into governed, usable data that supports measurable outcomes. You will operate as a cross-functional leader, balancing long-term architecture with near-term delivery needs. Success in this role requires strong judgment, clear communication, and comfort navigating a matrixed organization. You will facilitate working sessions that align stakeholders on definitions, sourcing decisions, and product scope.

Requirements

5+ years of experience in data strategy, data architecture, data governance, or data product management in a complex enterprise environment.
Extensive data modeling experience.
Demonstrated ability to read and critique conceptual and logical data models (including entity-relationship modeling).
Technical proficiency: Strong command of SQL; relational and dimensional database design (normalization, star/snowflake schemas); hands-on experience with modeling tools such as ERwin, PowerDesigner, or ER/Studio.
Familiarity with data warehousing concepts, ETL processes, and Python for automation is a plus.
Analytical & conceptual thinking: Ability to abstract complex business processes into structured data models across conceptual, logical, and physical levels; define entities, relationships, constraints, and patterns with precision.
Domain & methodology knowledge: Understanding of the business domain being modeled and familiarity with established modeling methodologies to select the right approach for organizational needs.
Data governance & quality awareness: Knowledge of metadata management, data lineage, master data management, and data quality standards to support trustworthy, well-governed, and compliant data assets.
Communication & collaboration: Ability to translate technical models for non-technical stakeholders, gather requirements effectively, and work across engineering, analytics, and business teams to align architecture to organizational goals.
Working knowledge of governance concepts, including domains, ownership, stewardship, master/reference data, lineage, and controls.
Demonstrated experience translating business requirements into data product requirements and specifications.
Experience partnering with engineering and product teams to deliver governed data products.
Experience working across multiple business functions and data domains in a matrixed environment.
Ability to lead workshops and stakeholder working sessions to align on definitions and scope.
Ability to identify authoritative data sources, resolve source conflicts, and document sourcing decisions.
Familiarity with data cataloging and/or modeling tools used in enterprise environments.
Strong written and verbal communication skills, including the ability to explain technical topics to non-technical audiences.

Nice To Haves

Experience implementing enterprise-scale data governance programs and operating models.
Proficiency in SQL for data validation and issue triage.
Hands-on familiarity with tools such as Amazon Web Services, ERwin, Databricks, or Unity (or comparable platforms).
Experience defining or operating data product taxonomies and certification/quality signals in a catalog.
Experience with metadata management frameworks and data lineage tooling.
Experience building or managing a use-case intake and prioritization process tied to measurable outcomes.
Exposure to master data management and reference data practices across domains.

Responsibilities

Audit relationships between data products to ensure entities, attributes, and models are consistently defined across domains.
Map cross-product dependencies and identify architectural gaps, redundancies, and misalignments.
Define and enforce semantic consistency for shared concepts, including definitions and lineage expectations.
Partner with data engineers and data modelers to establish standards for data quality, normalization, and maintainability.
Maintain an enterprise view of active data domains and ensure domain ownership and coverage are clear.
Lead domain completeness reviews and surface missing products and priority gaps to domain leaders.
Establish and maintain data product classifications (authoritative vs. derived; curated vs. raw; operational vs. analytical).
Develop and maintain data sourcing strategies, including authoritative source identification and conflict resolution.
Facilitate discovery sessions and working groups to align scope, definitions, and sourcing plans.
Translate technical concepts into clear business language and drive decisions to closure.
Build and maintain a use-case repository to support roadmap planning, prioritization, and outcome measurement.