Wealth Management - Senior Lead Data Architect - Vice President

Lead Data ArchitectJoin a team that develops high-quality data architecture solutions to deliver governed, high-quality, AI-ready data for business-critical applications and use cases. Help shape the global target-state architecture through capabilities across multiple data architecture domains.Job SummaryAs a Lead Data Architect at JPMorgan Chase within the Private Bank Chief Data & Analytics Office, you are an integral part of the team that develops new data-product offerings and leads end-to-end data-product design. Leverage AI knowledge to create metadata artifacts, develop AI skills to automate tasks, and deliver Data Architecture as a Product.Lead conceptual, logical, and physical data modeling within and across sub-domains. Create and maintain metadata artifacts such as business terms, glossaries, ontologies, taxonomies, and semantic/context layers for AI. Use modeling tools, such as Erwin, and AI-assisted techniques to accelerate metadata production while preserving governance rigor.Job ResponsibilitiesEngage technical teams and business stakeholders to discuss and propose data architecture approaches to meet current and future needsDefine the data architecture target state of the product and lead achievement of the strategyParticipate in data architecture governance bodiesExecute creative data architecture solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problemsOwn domain modeling and data design, including CDM/LDM/PDM for WM domains; model key entities with clear keys, relationships, and lifecycle statesDefine the semantic/context layer, including canonical business concepts and relationships; establish conformed dimensions and standardized measures (e.g., AUM, net flows, time-weighted return) with unambiguous definitionsProduce and maintain metadata artifacts, including business glossaries (terms, definitions, stewardship), ontologies/taxonomies (concept hierarchy, relationships), context layer mappings (technical fields ? business concepts ? metrics), naming standards, and modeling conventions. Incorporate metadata into the data mesh delivery process, aligned with governance policy expectationsApply AI to accelerate metadata production and maintain metadata at scale (e.g., suggested term mappings, documentation generation, relationship inference), with human review gates and version controlDesign for AI readiness to ensure models support analytics and AI use through stable identifiers, entity resolution approaches, history (SCD/event modeling), feature-friendly structures, and clear semantics for retrieval and groundingDefine standards for interoperability and evolution, including patterns for schema evolution, versioning, deprecation, and backward compatibility; ensure interoperability across platforms (warehouse/lakehouse/APIs/BI)Required Qualifications, Capabilities, and SkillsDemonstrate 5 years of experience or equivalent expertise in data design, including data products, data lakes or data warehouses, and data modelingApply advanced knowledge of the data-product development life cycle, design, and data analytics within a domain-driven, data mesh paradigmDemonstrate in-depth knowledge of the financial services industry and data ecosystemDemonstrate data modeling expertise and the ability to move fluidly from concept to logical to physical implementationApply practical cloud-native experienceUse AI tools to define requirements, shape design, and harvest and create metadataPreferred Qualifications, Capabilities, and SkillsDemonstrated prior experience working in a highly matrixed, complex organizationWealth management domain expertise, including knowledge of IBOR, ABOR, investments and securities, alternatives, derivatives, fixed income, and equitiesHands-on data profiling and analytics fluency with strong SQL skillsGraph database design knowledgeRisk/privacy awareness, including entitlements, data minimization, and classification, with the ability to partner effectively with controls teams