You’ll work across business and technology partners to define clear meaning, consistent definitions, and durable models that scale. You’ll leverage AI to automate data architecture design workflows and deliver “Data Architecture as a Product”. If you enjoy turning complexity into clarity—and making data easier to discover, understand, and use—this 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