Module 3 · Python for Data Engineering
Not a Python course. A DE-libraries-for-people-who-already-know-Python course. pandas (for DE use, not analyst use), snowflake-connector-python, boto3 patterns. Interview-ready in one focused sitting.
Required skill · s3 (Python half)
Python plus an ELT platform such as Matillion. · Matillion half → Module 5.
Your coverage: Strong (Python) + Partial (DE libs)
What we're assuming
- Years of Python scripting for ops, automation, and custom tooling.
- Experience with earlier-career ETL scripting in Python and Bash.
- You understand environments, packaging, venv/uv/pip, typing, logging — standard senior-dev fare.
What we modify for your background
- Skip: Python syntax, list comprehensions, decorators, context managers, packaging basics.
- Emphasize: pandas patterns for DE (not analyst plotting) — bulk ingest, dtype control, memory management on multi-GB frames.
- New:
snowflake.connector,write_pandas, Snowpark Python dataframes, boto3 pagination gotchas.
Want more depth?
See Credits → Module 3. For pandas mastery: Python for Data Analysis (McKinney). For Snowpark: the Snowpark Python developer guide.
Lessons
- Lesson 3.1 — pandas Fluency for Data Engineers crash deep
- Lesson 3.2 — snowflake-connector-python + write_pandas crash deep
- Lesson 3.3 — boto3 Patterns for DE Pipelines deep