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Role Description

This role involves leading the design and implementation of a data strategy and AI enablement engagement for a client.

• Lead canonical data model design — entity-relationship models for core business domains
• Oversee and interpret schema discovery across ~35 complex legacy Databricks datasets in a Medallion Architecture
• Reverse-engineer existing data structures and infer relationships across systems
• Establish standardized naming conventions, data definitions, and source-to-canonical mappings
• Collaborate with the client's data team to understand data structures, calculated fields, and transformation logic
• Design the semantic layer and data governance framework (Phase 2)
• Provide architectural direction for digital origination data model (Phase 3)
• Advisory input on ML-ready data products (Phase 4)

Qualifications

• Strong data modeling and schema design experience (conceptual, logical, physical)
• Experience with Databricks and/or Spark-based data platforms
• Demonstrated ability to reverse-engineer undocumented schemas and infer entity relationships
• Experience building canonical or enterprise data models in complex, multi-source environments
• Semantic layer design experience (ideally with Power BI as the downstream consumer)
• Comfortable working with business stakeholders to define data definitions and governance policies
• Eager to learn AI tooling

Requirements

• Financial services, lending, or banking data experience (strongly preferred)
• Experience with Medallion Architecture (bronze/silver/gold patterns) (strongly preferred)
• Familiarity with loan accounting systems or loan origination systems (strongly preferred)
• Experience with modern AI tool sets (strongly preferred)

Environment

The client's environment is managed with strict security controls. Access is through VDI (Windows) RDP into a dedicated server Databricks. Internet access on work servers is limited. You must be comfortable working within these constraints.