Technically, you can find data engineering roles that rely mostly on SQL and low-code tools. In practice, Python shows up in the vast majority of data engineering job descriptions in India, and for good reason — it handles the things SQL simply cannot.
What Python actually does in a data engineering workflow
SQL is excellent at transforming data that is already inside a database. But data does not always arrive nicely in a database. It comes from REST APIs, flat files, event streams, third-party tools, and systems that speak in formats SQL has no idea how to handle. Python bridges those gaps.
On a typical day, a data engineer might use Python to pull data from an external API and write it to S3, to read a CSV file, validate its structure, and load clean rows into a warehouse, to write an Airflow DAG that schedules and monitors a multi-step pipeline, or to add data quality checks that flag anomalies before they reach downstream consumers. None of that is advanced programming — but it is regular work.
How deep does your Python need to be?
For most junior and mid-level data engineering roles, the bar is practical, not theoretical. You need to be comfortable with:
- Variables, data types, and control flow
- Functions and error handling
- Working with files and directories
- HTTP requests and consuming APIs
- Pandas for reading and transforming tabular data
- Database connectivity — psycopg2, SQLAlchemy, or equivalent
You do not need to understand advanced object-oriented design patterns, async programming, or machine learning libraries. Those come later, if your role grows into them.
SQL first, then Python
If you are new to both, start with SQL. It gives you immediate feedback — you write a query, you see data — and the learning curve is gentler. Once you can comfortably write multi-table queries with window functions, move to Python. At that point you already understand the shape of data, which makes learning Pandas and pipeline logic significantly easier.
The combination of solid SQL and practical Python is enough to start applying for data engineering roles. Everything else — Spark, Airflow, cloud platforms — builds naturally on top of that foundation.