Reproduce a descriptive statistics report end-to-end with traceability to raw sources.
Profile and reconcile differing source schemas into a conformed model.
Build dbt staging, intermediate, and mart models with tests.
Write Great Expectations suites and wire into the pipeline to fail bad data.
Implement entity/identity resolution across sources.
Orchestrate pipelines with Airflow/Step Functions for repeatable runs.

🎯 Requirements

Strong SQL and Python for large-scale data processing.
AWS data stack: S3, Glue, Lake Formation, Athena/Redshift, Spark, Step Functions.
Data modeling & semantic layer (dbt or equivalent); dimensional modeling.
Entity resolution across heterogeneous sources.
Data-quality & testing frameworks (Great Expectations, dbt tests) and data lineage.
Big-data processing (Spark) with performance and cost optimization.

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