Location: Jersey City, NJ, United States

Youll work across business and technology partners to define clear meaning, consistent definitions, and durable models that scale. Youll leverage AI to automate data architecture design workflows and deliver Data Architecture as a Product. If you enjoy turning complexity into clarityand making data easier to discover, understand, and usethis role offers high visibility and strong growth opportunities.

Job summary 
As a Lead Data Architect in our wealth management data architecture team, you define the conceptual, logical, and physical data models for domains and data products. You create and maintain data models, taxonomies, business glossaries, metadata, and a semantic layer so teams can build analytics and artificial intelligence solutions with confidence. You partner with stakeholders to align on canonical business concepts, stable identifiers, and clear definitions that make data easier to find, interpret, and govern. You help shape the target-state architecture by setting standards that enable interoperability and sustainable evolution over time.  

Job responsibilities 

    Engage engineering teams and business stakeholders to propose data-architecture approaches that meet current and future needs 
    Define the target-state data architecture for owned data products and drive delivery against the strategy 
    Participate in data-architecture governance forums and ensure alignment to standards and controls 
    Design and troubleshoot architecture solutions, applying creative thinking beyond routine or conventional approaches to solve complex technical problems 
    Own conceptual, logical, and physical data models for wealth management domains, including keys, relationships, and lifecycle states 
    Define canonical business concepts and relationships, including conformed dimensions and standardized measures (for example, assets under management and net flows) 
    Produce and maintain metadata artifacts (business glossary, taxonomy, semantic/context layer mappings, naming standards, modeling conventions) and embed them into data mesh delivery processes 
    Design for AI readiness: Ensure models support analytics/AI use: stable identifiers, entity resolution approach, history (SCD/event modeling), feature-friendly structures, and clear semantics for retrieval and grounding 
    Establish patterns for schema evolution, versioning, deprecation, and backward compatibility across platforms (warehouse, lakehouse, application programming interfaces, business intelligence)

Required qualifications, capabilities, and skills 

    (Option A regions where years are permitted) 5+ years of experience or equivalent expertise in data design for data products, data lakes, or data warehouses 
    (Option B EMEA-style) Demonstrable experience in data design for data products, data lakes, or data warehouses 
    Advanced knowledge of data-product development lifecycles, design practices, and analytics within a domain-driven, data mesh paradigm 
    Demonstrable data modeling experience with ability to move from conceptual to logical to physical implementation 
    Practical cloud-native experience in designing and delivering data solutions 
    Experience defining and maintaining metadata (for example, glossary terms, definitions, and mappings) with governance discipline 
    Ability to partner effectively with business stakeholders and technical teams to translate requirements into durable data designs 
    Familiarity with using automation or artificial intelligence tools to improve documentation quality, metadata coverage, or design workflows 

Preferred qualifications, capabilities, and skills 

    Experience working in a highly matrixed, complex organization 
    Wealth management domain expertise, especially client onboarding and lifecycle processes (for example, customer relationship management, know your customer, onboarding, client servicing) 
    Strong data profiling and analytics fluency, including SQL skills 
    Experience with graph data modeling or graph database design 
    Risk and privacy awareness (for example, entitlements, data minimization, data classification) and ability to partner effectively with controls teams 
 

Shape wealth management data models, metadata and semantics to deliver governed, AI-ready data products.