data-warehousing
Best practices for designing and reviewing data warehouses and analytical pipelines using the mandatory bronze/silver/gold medallion architecture, validations-as-code, idempotent transforms, explicit table grain and keys, and in-pipeline data-quality checks. Use for ETL/ELT jobs, dbt models, Airflow DAGs, SQL warehouses, lakehouses, source ingestion, table modeling, and layered analytics workloads in DuckDB, Snowflake, BigQuery, Postgres, Spark, and similar systems. Pair implementation reviews with code-review.
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- balintdecsi/skills
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- October 19, 2026 at 15:05
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SKILL.md
Source instructions · Read-only preview| name | data-warehousing |
|---|---|
| description | Best practices for designing and reviewing data warehouses and analytical pipelines using the mandatory bronze/silver/gold medallion architecture, validations-as-code, idempotent transforms, explicit table grain and keys, and in-pipeline data-quality checks. Use for ETL/ELT jobs, dbt models, Airflow DAGs, SQL warehouses, lakehouses, source ingestion, table modeling, and layered analytics workloads in DuckDB, Snowflake, BigQuery, Postgres, Spark, and similar systems. Pair implementation reviews with code-review. |
