Python for Data Engineering focuses on using Python to collect, process, and manage large datasets efficiently. It involves working with libraries like Pandas, NumPy, and PySpark for data manipulation and transformation. Data engineers use Python scripts to automate ETL (Extract, Transform, Load) pipelines and integrate data from multiple sources such as APIs and databases. Tools like Airflow and SQLAlchemy help in workflow automation and data management.