Become a Data Engineer

Data Engineering

Build the pipelines that power data teams — from advanced SQL and Python ETL through Snowflake, dbt, PySpark, Airflow, Kafka, and Docker. 12 modules covering the complete modern data stack.

10 months

13 Projects + 2 Capstones

Live Course

SQLPythonSnowflakedbtPySparkAirflowKafkaDockerAWSGreat Expectations

Roles you'll be ready for

Data EngineerAnalytics EngineerETL DeveloperCloud Data Engineer

Industry range: ₹5–10 LPA(fresher, India — market benchmark, not a guarantee)

Modules

Duration

10 months

Mode

Live Online

Projects

13 Projects + 2 Capstones

Support

Career Prep

Curriculum

13 modules. Each module includes a hands-on project. The final module contains your capstone assignments.

🚀 Opening Weeks

Opening Weeks — SQL & Git Foundations

22Topics

No prior SQL or Git assumed. These three weeks build the two non-negotiable foundations the entire track runs on — SQL from scratch and Git for team collaboration.

What a relational database is — tables, rows, columns, primary and foreign keys

Installing PostgreSQL and connecting with pgAdmin or TablePlus

SELECT, FROM, WHERE — filtering with all comparison and null operators

ORDER BY, LIMIT, DISTINCT; COUNT, SUM, AVG, MIN, MAX

GROUP BY, HAVING — and the common mistake of using WHERE when you need HAVING

Conditional aggregation — COUNT(CASE WHEN ... THEN 1 END) pattern

INNER JOIN, LEFT JOIN — understanding the ON clause, what each row represents

Multi-table joins; subqueries in WHERE; WITH clause (CTE)

CASE WHEN — conditional logic inside a query; COALESCE for null handling

Top N per group pattern, period comparison patterns

The grain concept — what does one row in this result represent?

What version control is and why data engineers cannot live without it

git init, git clone — creating and getting a repository

git add, git commit -m — staging changes and saving a snapshot

git push, git pull — sending and receiving changes from the team

.gitignore — excluding credentials, large data files, virtual environments

git checkout -b — creating and switching to a new branch

git merge — combining branches; understanding and resolving merge conflicts

Pull requests — the code review step before merging to main

The DE team workflow — no one commits directly to main

Conventional commits — feat:, fix:, chore:, docs: — writing useful commit messages

GitHub Actions first look — triggers, jobs, steps; why automated checks matter

Module 1

SQL Mastery

1 Sample Project18Topics

Advanced SQL beyond tutorials — window functions with ROWS/RANGE frames, recursive CTEs, query optimisation, and the dimensional modelling decisions that determine warehouse maintainability.

Module 2

Python for Data Engineering

1 Sample Project16Topics

Python in data engineering means robustness, error handling, and testability — not interactivity.

What you will learn

Write production-grade SQL — window functions, recursive CTEs, optimisation for billion-row tables

Build robust Python ETL pipelines with error handling, logging, and pytest test suites

Design star schema dimensional models with SCD Type 2 in Snowflake

Build dbt projects with tested, documented, version-controlled transformation models

Process large datasets with PySpark DataFrames, Delta Lake, and Spark UI tuning

Orchestrate pipelines with Airflow DAGs, idempotent design, and automated alerting

Build real-time event processing pipelines with Kafka, ksqlDB, and Kafka Connect

Package and deploy pipelines with Docker, CI/CD, and automated Great Expectations checks

Job outcomes

Data EngineerAnalytics EngineerETL DeveloperCloud Data Engineer

Industry range:₹5–10 LPA

Frequently asked questions

Ready to start your Data Engineering journey?

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