Wealth Management - KYC Data Product Manager - Vice President

Leading the future of Wealth Management compliance data, the KYC Data Product team builds and operates foundational data capabilities that power client onboarding, regulatory compliance, and risk management at scale.

 Job Summary

As a KYC Data Product Manager (Vice President) in JP Morgan's Wealth Management division, you will lead the end-to-end strategy, delivery, and operational excellence of KYC data products – from API-based ingestion and integration, through persistence and quality controls, to distribution via batch extracts and GraphQL. You will guide scalable reuse through strong data modelling discipline, measurable data quality, and AI-assisted SDLC practices that improve speed and consistency while meeting control and regulatory expectations.

Job responsibilities

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  Define multi-quarter vision and roadmap for KYC data products across ingestion, storage, and distribution, balancing consumer outcomes, regulatory and control needs, and platform health.



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  Set the conceptual and logical modelling direction for KYC data (canonical concepts, boundaries, definitions, relationships) ensuring alignment between API contracts, persistence models, and distribution schemas.



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  Own the KYC distribution product portfolio including delivery patterns, SLAs and SLOs, schema and versioning policy, deprecation approach, and consumer onboarding model. 

  
  

   



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  Create and enforce product contracts, data access and entitlements patterns, privacy and PII controls, retention, auditability, and change governance for KYC data flows. 



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  Partner with Architecture, Data Governance, Risk, Compliance, and Controls to embed requirements into designs early. 



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  Establish a KPI framework for end-to-end KYC data health: freshness, completeness, duplication, reconciliation pass rates, consumer defect rates, and adoption and reuse. 



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  Execute a repeatable quality operating cadence with dashboards, thresholds, escalation paths, and remediation backlog. 



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  Lead cross-team delivery across multiple squads and stakeholders, managing dependencies, risk, scope, and sequencing. 

  
  

   



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  Sponsor monitoring and alerting, runbooks, and postmortem discipline for KYC data services, guiding structural fixes and resilience improvements. 



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  Establish expectations and guardrails for using AI in SDLC (requirements, test design, documentation, analytics support) with appropriate human validation and control compliance. 



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  Mentor Area Product Owners, set performance expectations, and build capability in modelling, analytics, and product craft

Required qualifications, capabilities, and skills

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  8+ years in Product Management, Product Ownership, or Data Product leadership, including leadership of cross-functional delivery in complex environments. 



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  Demonstrated ownership of end-to-end data products (ingestion and integration through persistence to distribution). 



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  Strong fluency in conceptual and logical data modelling with ability to sponsor canonical models and guide schema evolution. 



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  Proven track record delivering and operating batch distribution and API-based consumption models (GraphQL strongly preferred). 



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  Track record establishing SLAs and SLOs, operational metrics, and reliability practices for data delivery services. 



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  Strong governance orientation: privacy and PII, entitlements and access, auditability, retention, and controlled change. 



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  Demonstrated ability to use and institutionalize AI-assisted SDLC practices with appropriate templates, review discipline, and quality controls

Preferred qualifications, capabilities, and skills

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  KYC domain experience including client identification, verification, due diligence, risk rating, entity resolution, and periodic review processes. 



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  Familiarity with regulatory frameworks such as AML, BSA, CDD, and EDD requirements. 



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  Experience modernizing from legacy feeds to curated data products including rationalization and deprecation at scale. 



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  Experience with metadata, lineage, catalogue adoption, and data product discoverability. 



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  Comfort with analytics and data science methods for profiling, anomaly detection, and impact sizing. 



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  Experience in wealth management or related financial services. 



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  Strong written and verbal communication skills with ability to influence without authority across engineering and domain leadership