Senior Product Associate - Data Management

JPMorganChase•Plano, TX

About The Position

Join the Consumer & Community Bank's Data & Analytics Data Management team to build capabilities that ensure AI readiness for a large data estate. The role helps shape products that evaluate and enrich catalog metadata, modernize data modeling, and monitor data health. Users include data owners, stewards, engineers, analysts, and AI agents. The Senior Product Associate works with the Product Manager and cross-functional partners to identify customer and market needs, shape product opportunities, and support product development and ongoing improvement. The work combines user research, product metrics, hands-on SQL and data analysis, design and AI tools, and close collaboration with engineering, governance, platform, and other stakeholders. The associate helps make data-management capabilities useful for governance, access, analytics, and AI readiness, and supports solutions from discovery through launch and post-launch measurement.

Requirements

At least 3 years of experience or equivalent expertise in product management or a relevant domain area.
Proficient knowledge of the product development life cycle.
Experience with product life cycle activities, including discovery and requirements definition.
Developing knowledge of data analytics and data literacy.
Strong product management fundamentals, including discovery, prioritization, road-mapping, backlog management, and delivery across the product life cycle.
Excellent communication skills and interest in engaging users, presenting, and giving demos; hands-on collaboration with partners to deliver scalable solutions.
Experience working in a human-centered product development process, including user research, usability testing, and iteration based on feedback.
Depth in data management, governance, and/or analytics, such as metadata, data quality, classification, or stewardship.
Working SQL skills and understanding of data system fundamentals such as schemas, data models, and pipelines.
Regular use of agentic tools such as Claude, Codex, or Copilot for research, analysis, writing, and prototyping, with judgment about when human review is needed.

Nice To Haves

Experience taking a product from discovery through launch and post-launch iteration, with measurable results.
Experience with data modeling, data catalogs, lineage, or metadata platforms such as DataHub, Collibra, or Alation.
Experience building AI-powered products in a regulated environment.
Experience in financial services or another regulated industry.
Experience with data analytics, reporting, quality measurement, publishing, observability, and governance.

Responsibilities

Partner with the Product Manager to identify product opportunities that reflect customer and market needs through user research and discovery.
Consider upstream and downstream implications of new features for the overall product experience.
Support user research, journey mapping, and market analysis to inform the product roadmap and identify valuable potential features.
Analyze, track, and evaluate product metrics, including performance against time, cost, and quality targets throughout the product development life cycle.
Write requirements, epics, and user stories to support product development.
Find and prioritize high-value opportunities using a commercial mindset and firm and partner capabilities.
Build relationships with users, engineering partners, and stakeholders; conduct interviews and usability testing to understand user needs.
Learn how metadata, data health, and data models support governance, access, analytics, and AI readiness, and connect product decisions to those outcomes.
Create clear documentation, presentations, and demos, and influence governance, platform, and engineering partners without formal authority.
Use SQL, data analysis, design, and AI tools to investigate data, prototype ideas, and inspect and constructively challenge solution designs.
Maintain a high-quality, prioritized backlog of clear, testable epics and stories; partner with engineering on priorities, trade-offs, scrum events, and blockers.
Reduce delivery risk by validating early with prototypes, spikes, and pilots, and testing throughout development and before release.
Support product launches and post-launch work by tracking adoption, outcomes, and value; triaging feedback; and favoring scalable, reusable solutions built with partners.