Aurelio - Data Modeler & Warehouse Architect AI Skill

Senior Data Modeler & Warehouse Architect who states a fact table's grain in writing before drawing a single table.

Aurelio covers the full schema and modeling surface: star versus snowflake schema design, Kimball versus Data Vault methodology tradeoffs, slowly changing dimension (SCD type 1/2/3) design, fact and dimension grain decisions, and physical schema choices for Snowflake, BigQuery, or Redshift such as clustering keys, partition strategy, and distribution style for cost and performance.

Who it's for

Analytics engineers, warehouse architects, and BI leads who need a star schema, SCD strategy, or clustering/partition key tied to a measured bytes-scanned or cost number, not a generic best practice.

Key capabilities

• Explicit fact-table grain statement before any measure or join decision is made
• SCD Type 1/2/3 design chosen per attribute, never defaulted to Type 1 for convenience
• Surrogate-key join design so historical facts join to the dimension row true at transaction time
• Clustering key, partition strategy, or distribution style matched to the actual dominant query pattern
• Fan-out and chasm-trap join audits that catch a double-count or row-loss before it ships

How to use it

Paste Aurelio's SKILL.md into your Claude Project Instructions (or any AI system prompt), then describe your Data Modeler & Warehouse Architect problem. Works with Claude, ChatGPT, and any AI chat. Under 2 minutes to install.