Technical Product Manager - Data Products

Are you a technically fluent product leader who thrives at the intersection of data, engineering, and business strategy? Join a team where your ability to translate complex systems into scalable, intelligent products will directly shape how the firm manages, governs, and activates its most critical data assets.

As a Product Manager at JPMorganChase within the Data Products Organization, you will own the full product lifecycle for digital, data, and analytics products — from discovery through production. You will partner with engineering, architecture, data science, and compliance teams to deliver reliable, scalable data capabilities that meet both business needs and regulatory standards. Your work will directly influence how the firm leverages enterprise data, APIs, and AI-enabled experiences to drive measurable outcomes.

Job responsibilities

- Define and maintain product vision, roadmaps, objectives and key results, and adoption measures that align stakeholder priorities with delivery execution
- Lead Agile Scrum and Kanban ceremonies; manage backlogs, epics, features, user stories, acceptance criteria, dependencies, risks, milestones, and releases using tools such as Jira, Jira Align, or similar platforms
- Analyze application services, digital APIs, database logic, JSON payloads, and source dependencies to translate embedded logic into product requirements, technical designs, and reusable capabilities
- Build and validate data pipelines and prototypes using SQL, Python, and PySpark to support data discovery, transformation, and feature analysis
- Design conceptual, logical, physical, and polyglot data models; publish and manage data products across cloud warehouse and data lake environments
- Develop domain ontologies, semantic models, and AI-enabled retrieval workflows, including agentic interfaces, tool orchestration, and model context protocol services
- Embed data quality, lineage, security, controls, traceability, and regulatory requirements throughout the product delivery lifecycle
- Partner cross-functionally with engineering, architecture, data science, compliance, governance, and program management teams to deliver scalable and trustworthy products
- Communicate product strategy, technical decisions, and delivery status clearly to both implementation teams and senior stakeholders

Required qualifications, capabilities, and skills

- Formal training or certification on product engineering concepts and 5+ years applied experience
- 3+ years of experience delivering digital, data, analytics, or technology products within financial services, consulting, technology, or another highly regulated industry
- 3+ years of hands-on experience with SQL, Python, and PySpark for data discovery, transformation, pipeline development, testing, and delivery of production-grade products
- 3+ years of experience analyzing application services, APIs, and JSON structures, including service tracing, schema analysis, and translation of embedded logic into product and data requirements
- 3+ years of hands-on experience with cloud-based data warehouse and data lake platforms, including building, publishing, transforming, and consuming data products
- 2+ years of experience with logical and physical data modeling tools, including entities, relationships, schemas, keys, and source-to-target mappings
- 2+ years of experience with polyglot data modeling tools across relational, JSON, API, and NoSQL structures
- 3+ years of project management experience using Agile delivery tools, including delivery planning, risk and dependency management, milestone tracking, and governance reporting
- Demonstrated experience developing and deploying enterprise-grade generative AI and agentic solutions, including integration with enterprise data, APIs, security controls, and monitoring

Preferred qualifications, capabilities, and skills

- Advanced degree in Data Science, Information Systems, or a closely related discipline
- Experience with semantic retrieval, structured outputs, function calling, tool orchestration, evaluation frameworks, and model explainability
- Familiarity with data mesh, data product publishing patterns, and open table formats such as Apache Iceberg
- Ability to engage with engineers and architects at implementation depth while translating technical decisions into clear business narratives for senior audiences
- Experience working within a regulated financial services environment with exposure to data governance, lineage, and compliance frameworks