Data Transformation Transform raw data into analytical assets using modern transformation patterns, frameworks, and orchestration tools. Purpose Select and implement data transformation patterns across the modern data stack. Transform raw data into clean, tested, and documented analytical datasets using SQL (dbt), Python DataFrames (pandas, polars, PySpark), and pipeline orchestration (Airflow, Dagster, Prefect). When to Use Invoke this skill when: - Choosing between ETL and ELT transformation patterns - Building dbt models (staging, intermediate, marts) - Implementing incremental data loads…