python-data-engineering
Python Data Engineering Skill
Expert guidance for Python data engineering: DataFrame libraries (Polars, Pandas, PySpark), dbt Python models, API extraction, and data validation. Assumes Python proficiency.
Scope Constraints
- SQL transforms in dbt: hand off to
dbt-transforms - DLT pipeline config: hand off to
data-integration - Kafka/Flink streaming: hand off to
event-streaming - Dagster/Airflow orchestration: hand off to
data-pipelines - General Python or web development: out of scope
When to Use
Activate when: choosing between DataFrame libraries, writing Polars/Pandas/PySpark transforms, building dbt Python models, building API extraction scripts, implementing data validation (Pydantic/Pandera/GX), optimizing DataFrame memory, or converting between DataFrame formats.
Model Routing
| reasoning_demand | preferred | acceptable | minimum |
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