Most “Learn Python for Data Engineering” courses stop at pandas. This one goes further: you’ll move from DataFrame transformations into PySpark for larger datasets, Airflow DAGs for scheduling, Git and CI/CD for shipping pipeline code safely, and cloud warehouses like Snowflake or BigQuery for the load step. Labs use real, messy datasets rather than clean sample CSVs, and the certification capstone mirrors what a hiring manager would actually ask you to walk through.
Prerequisites
- Basic programming logic – loops, functions, conditionals – in any language
- Comfort with SQL fundamentals: SELECT, JOIN, GROUP BY
- Familiarity with using a command line or terminal
- No prior Python experience required – covered from the ground up in Module 1
- A laptop able to run Python 3.11+ and Docker for the lab environment
Course Objectives
- Write clean, production-quality Python for data ingestion and transformation
- Build batch and streaming ETL/ELT pipelines using pandas, PySpark, and Polars
- Orchestrate multi-step workflows with Apache Airflow DAGs
- Apply testing, logging, and error-handling practices that hold up in production
- Connect Python pipelines to cloud warehouses – Snowflake, BigQuery, Redshift
- Package and deploy pipeline code using Git, CI/CD, and containers
- Design pipelines with cost and reprocessing overhead in mind
- Complete a portfolio-ready capstone project for the certification
What You Will Learn
- Python fundamentals refreshed specifically for data workflows, not generic syntax drills
- pandas and Polars for in-memory transformation at scale
- PySpark for distributed processing on larger datasets
- Building and scheduling Airflow DAGs, including sensors, retries, and backfills
- Writing modular, testable pipeline code with pytest, type hints, and logging
- Working with structured and semi-structured data – Parquet, JSON, Avro
- Loading data into Snowflake, BigQuery, or Redshift from Python
- API-based data ingestion and building lightweight data services
- Git-based version control and CI/CD for pipeline deployment
- Data quality checks and pipeline monitoring after go-live
- A cost-conscious approach to pipeline design that employers increasingly expect
Who Should Take This Course?
This course works best for people who can already write some code or SQL and now want to move specifically into building data pipelines for a living.
- Aspiring data engineers coming from analyst, BI, or QA backgrounds
- Software developers pivoting into data-focused roles
- Data analysts who want to own the pipeline, not just the dashboard
- SQL developers looking to add Python and orchestration skills
- Computer science graduates targeting data engineering as a first role
- Working professionals preparing for a Python for Data Engineering certification
Skills You Will Gain
- Writing clean, reusable Python that handles messy real-world data – missing values, inconsistent types, malformed files – without falling over.
- Designing ETL/ELT jobs that are testable, restartable, and easy to debug when something breaks in production rather than in a demo.
- Scheduling and monitoring pipelines in Airflow, plus the Git and CI/CD habits that keep pipeline code deployable and reviewable by a team.
Tools Covered
- Python 3.11+
- pandas and Polars
- PySpark
- Apache Airflow
- Git and GitHub Actions
- Docker
- Snowflake / BigQuery (cloud warehouse labs)
- Pytest
Career Outcomes
Python, SQL, and hands-on pipeline-building show up as the baseline requirement across almost every data engineering posting right now, and this course is built directly around that combination.
- Data Engineer
- Python Data Engineer
- ETL / ELT Developer
- Data Pipeline Engineer
- Analytics Engineer
- Big Data Engineer (Python + Spark track)
Average salaries of Python data engineers
| Job Role | Experience Level | India | USA |
|---|---|---|---|
| Python Data Engineer | Entry Level (0-2 years) | ₹5-8 LPA | $88K-$110K/year |
| Data Engineer | Entry to Mid-Level (1-3 years) | ₹6-10 LPA | $110K-$130K/year |
| ETL Developer | Mid-Level (2-5 years) | ₹7-12 LPA | $115K-$140K/year |
| Data Pipeline Engineer | Mid-Level (3-6 years) | ₹9-15 LPA | $120K-$155K/year |
| Senior Data Engineer | Senior (5-10 years) | ₹12-20 LPA | $140K-$180K/year |
| Lead / Principal Data Engineer | Senior (8+ years) | ₹18-30+ LPA | $160K-$210K+/year |
Why Choose kodestree?
A few things make this course worth your time over a generic Python tutorial:
- Instructor-led sessions kept to small batch sizes
- Labs built on real, messy datasets instead of clean sample CSVs
- Capstone project designed to double as a portfolio piece
- Lifetime access to recorded sessions and course material
- Weekday, weekend, and 1-on-1 formats available
- Placement assistance and interview preparation support
