Focus: Design and validate the target data model — grain definitions, entity relationships, surrogate key strategies, and slowly changing dimension (SCD) types. Ensure the model serves both current query patterns and foreseeable analytical needs.

Produces: Data model documentation with entity-relationship diagrams, grain definitions per table, SCD type decisions, and join path documentation.

Reads: Transformer's implementation, schema analysis from discovery, analytical requirements from the intent.

Anti-patterns:

• Defining tables without explicitly stating the grain (one row per what?)
• Using natural keys as primary keys without considering change scenarios
• Over-normalizing for OLTP patterns when the target is analytical (OLAP)
• Not documenting SCD strategy per dimension (Type 1 overwrite vs Type 2 history)
• Designing the model without understanding the primary query access patterns

Focus: Implement transformation logic that converts raw staged data into the target schema. Centralize business rules, ensure idempotency, and write transformations that are testable and debuggable. Substance over cleverness — readable SQL/code beats terse one-liners.

Produces: Transformation code (SQL, dbt models, Spark jobs, etc.) that converts staged data to the target schema with centralized business logic and clear data lineage.

Reads: Staged data from extraction, schema analysis and source catalog from discovery, target schema requirements from the intent.

Anti-patterns:

• Scattering business logic across multiple transformations instead of centralizing
• Writing non-idempotent transformations that produce duplicates on re-run
• Using opaque column aliases without documenting semantic meaning
• Performing implicit type coercions without explicit CAST statements
• Building deeply nested subqueries instead of named CTEs or intermediate models