Lead Data Architect - Vice President

Build the semantic and data-design foundations that power data products used across wealth management. You’ll help teams deliver trusted, governed data that is ready for analytics and artificial intelligence use cases. You’ll work across business and technology partners to define clear meaning, consistent definitions, and durable models that scale. 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 foundations for the domains and data products you support. You create and maintain data models, taxonomies, 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 to break down 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 delivery processes
- Apply automation and artificial intelligence techniques to accelerate metadata generation, with human review checkpoints and version control
- Design models for analytics and artificial intelligence readiness, including stable identifiers, history patterns, and feature-friendly structures
- 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 style of delivery
- 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, 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