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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As a Card Data and Analytics - Data Product Owner Lead, you will join the Card Data & Analytics organization to lead the strategy and delivery of making firm data ready, well-contextualized, and trustworthy for AI and machine learning use. You will serve as a connective partner between data platform engineering, analytics engineering, and AI/ML teams to translate business problems into governed data products. Your daily work involves setting standards for data quality, lineage, and documentation while partnering with Product teams to shape roadmaps and prioritize investments. You must possess deep knowledge of data modeling, pipelines, modern data warehousing, and AI enablement patterns like retrieval and semantic layers. This role addresses the technical challenge of structuring and governing data within a regulated banking environment to ensure downstream systems can consume information safely and effectively.
Lead the strategy to prepare and contextualize firm data for AI and machine learning consumption.
Translate business problems into well-defined, governed data products for technical teams.
Establish standards for data quality, lineage, documentation, and readiness for downstream use.
Partner with Product teams to shape roadmaps and prioritize investments that deliver measurable business value.
Drive execution across multiple stakeholders to manage complex initiatives within firm deadlines.
Communicate technical details and business outcomes effectively to both engineering and executive audiences.
Enforce data governance, privacy, and risk controls within a regulated banking environment.
What we're looking for
Strong working knowledge of data platform engineering and analytics engineering concepts including data modeling, pipelines, and modern data warehousing.
Demonstrated fluency in the AI/ML space regarding how systems consume data and the role of context and metadata.
Proven ability to lead complex, cross-functional initiatives and hold teams accountable to timelines.
Exceptional organizational skills with the ability to bring structure to ambiguous, fast-moving work.
Strong communication and executive presence to influence across engineering, product, and business partners.
Solid product acumen and understanding of how data and AI translate into business outcomes.
Experience in financial services or another highly regulated industry (preferred).
Familiarity with modern AI enablement patterns such as retrieval, semantic/metadata layers, and grounding data for AI use (preferred).
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