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Engagement: Data Strategy & AI Enablement Location: Remote
Meaningful AI helps organizations unlock the value in their data. We design and build AI-driven data platforms, taking clients from raw, undocumented data environments through to canonical models, semantic layers, and analytics-ready infrastructure.
Design canonical data models for core business domains
Discover and interpret schemas across complex legacy datasets (like Databricks)
Reverse-engineer existing data structures and infer relationships across systems
Establish standardized naming conventions, data definitions, and source-to-canonical mappings
Collaborate with client data teams to understand data structures, calculated fields, and transformation logic
Design semantic layers and data governance frameworks
Provide architectural direction for downstream data models
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
Experience in regulated industries with complex data environments
Experience with Medallion Architecture (bronze/silver/gold patterns)
Familiarity with enterprise transaction and accounting systems
Our client environments are managed with strict security controls and restricted internet access.
You should be comfortable working within enterprise security constraints.
Originally posted on Himalayas