Data Engineering Bootcamp

Before the bootcamp kicks off, you’ll complete a self-paced pre-work module designed to build your foundation in Python, SQL, and data handling, alongside Git version control and core cloud concepts. You’ll practice coding, manipulating data with Pandas, running SQL queries, and understanding modern data workflows so you can transition smoothly into hands-on pipeline development from Week 1. By the end, you’ll be confident in Python scripting, data wrangling with Pandas, and navigating relational and cloud databases, ready to tackle real-world data challenges from day one.

Start strong by building the essential programming and data architecture skills every Data Engineer needs. You’ll learn Python for data handling, master Pandas and NumPy for transformation, and take your SQL to the next level. Then, dive into data modeling, from designing schemas and databases to understanding how data lakes and warehouses work together. By the end, you’ll build your first ETL pipeline and design your own data schema from scratch.

This module introduces you to building and automating data pipelines using Airflow and dbt. You’ll schedule transformations, track data lineage, and test workflows end-to-end. Then, you'll move into the world of big data with Apache Spark to process massive datasets efficiently and scale performance like a pro. You’ll also optimize PySpark transformations, explore lazy execution, and tune performance for distributed workloads. You’ll wrap up by delivering your first full data pipeline that powers real analytics systems.

In this module, you’ll learn to build and manage warehouses using Snowflake, BigQuery, or Redshift, automate infrastructure with Terraform and Docker, and design cost-efficient multi-cloud solutions. Your project will integrate ETL pipelines, dbt transformations, and BI reporting. Then, you’ll process live data with Kafka and Spark Streaming, manage scaling and fault tolerance, and explore cloud streaming services like Pub/Sub and Kinesis. By the end, you'll build a real-time data ingestion and analytics pipeline, visualized on a live dashboard, equipping you with hands-on experience in event-driven architectures and instant data insights.

In this module, you’ll focus on what makes a great Data Engineer: reliability, transparency, and impact. You’ll test and validate data quality using Great Expectations, track lineage with dbt, and apply governance best practices for privacy and compliance. Then comes your Capstone Sprint, a hands-on, production-grade project where you’ll design, build, and deploy a complete data system integrating real-time pipelines, governance, and analytics visualization.

By the end of the Data Engineering Bootcamp, you’ll complete production-grade capstone projects simulating real-world enterprise systems. You’ll design, build, and deploy pipelines that integrate real-time streaming, governance, and analytics visualization. On Part I, you'll create a real-time banking transaction pipeline, while on Part II you will focus on telecom CDR streaming and governance. Alongside your projects, you’ll practice Agile collaboration, Git workflows, technical communication, and interview preparation, ensuring you are job-ready.