AI Data Architect

AI Data Architect –

Job Description
Location: NYC, NY 

Although this role is remote, they are looking for candidates in New York or as far as NJ, CT, or PA. The role will require occasional client travel.

Client is seeking an experienced AI Data Architect to support a financial services client with enterprise data strategy and transformation. This role will focus on defining the data architecture, capabilities, and roadmap needed to enable AI, GenAI, and agentic AI initiatives.
The ideal candidate will combine strong enterprise data architecture and data strategy expertise with experience across MDM, customer data, data products, governance, and modern data access capabilities.
Required Skills
10+ years of experience in data architecture, data strategy, or enterprise data transformation.
Strong financial services experience.
Strong enterprise data architecture expertise.
Experience supporting AI, GenAI, or agentic AI data enablement.
Strong experience with MDM and customer data.
Experience with data products and enterprise data foundations.
Knowledge of APIs, data integration, and enterprise data access patterns.
Experience with data governance and operating models.
Understanding of semantic and contextual data capabilities, including metadata, taxonomies, ontologies, or knowledge graphs.
Experience conducting capability assessments, gap analyses, and roadmap development.
Strong executive workshop facilitation and stakeholder management skills.
Excellent PowerPoint, executive communication, and storytelling skills.
Key Responsibilities
Lead enterprise data strategy and transformation initiatives.
Assess current-state data capabilities and define target-state requirements.
Define enterprise data architecture to support AI and agentic AI use cases.
Evaluate and define MDM and customer data capabilities.
Define data products and foundational data capabilities.
Establish API and enterprise data access patterns.
Define semantic and contextual data capabilities, including metadata, taxonomies, ontologies, and knowledge graphs.
Define data governance and operating models.
Conduct capability assessments and identify key gaps and opportunities.