Data
Engineering

Master the modern data stack to design, build, and optimize scalable data pipelines, data warehouses, and big data architectures.

16 weeksIntermediateLive Projects
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Program HighlightsWhat You'll Experience
Live instructor-led sessions
1-on-1 mentorship
Real-world projects
Career guidance

Key Responsibilities

Develop and orchestrate fault-tolerant ETL workflows using Airflow
Build high-throughput big data processing pipelines utilizing PySpark and Kafka
Design efficient, scalable cloud-native data lake and warehouse schemas
Automate end-to-end CI/CD data flow deployments on AWS/Azure

Technologies You'll Master

Python
Python
SQL
SQL
Pandas
Pandas
Apache Spark
Apache Spark
Apache Airflow
Apache Airflow
Kafka
Kafka
AWS/Azure
AWS/Azure
Snowflake
Snowflake

Learning Outcomes

Design and implement scalable data pipelines for structured and unstructured data
Gain practical experience with large-scale distributed systems and real-time data streaming
Master ETL processes, data warehousing, and complex data pipeline orchestration
Build a robust portfolio showcasing end-to-end, cloud-deployed data architectures
Project Allocation Framework

Real-World Project Execution

As part of the Data Engineering Internship Program, project-based learning is a critical component designed to provide students with hands-on exposure to real-world data systems, pipelines, and scalable architectures. The objective of this framework is to ensure that students develop the ability to design, build, optimize, and deploy data workflows aligned with industry standards. The project structure follows a progressive model, categorized into five distinct sets based on complexity. The initial sets focus on foundational database operations and data handling. Intermediate sets introduce pipeline development, big data processing, and workflow orchestration. The final set consists of advanced, industry-level projects incorporating cloud platforms, streaming systems, and end-to-end deployment.

Learning Stages

  • Set 1 & Set 2: Foundational (Basic Level)
  • Set 3 & Set 4: Intermediate (Medium Level)
  • Set 5: Advanced (Industry-Level Capstone)

Implementation Guidelines

  • Follow a structured lifecycle: problem definition, data ingestion, cleaning, transformation, and storage.
  • Perform pipeline development, validation, optimization, and deployment.
  • Intermediate and advanced projects must incorporate Apache Spark, Apache Airflow, and Kafka.
  • Advanced projects must include deployment on AWS or Microsoft Azure ensuring exposure to scalable, production-grade environments.

Expected Outcomes

  • Design and implement robust data pipelines and manage structured/unstructured large-scale data workflows
  • Operate effectively with distributed systems and gain practical experience in ETL and real-time processing
  • Develop proficiency in modern industry tools like Kafka, Airflow, and Spark
  • Enhance problem-solving capabilities to build scalable data portfolios aligned with modern roles
Project Catalogue
Foundational Level (Basic – Level 1)

Project Set 1

This set focuses on basic data handling, file processing, and introductory database operations.

1
Student Records Management System using CSV
2
Sales Data Processing using Python
3
JSON Data Parser and Analyzer
4
Basic Log File Analyzer
5
Employee Data Management using SQL
6
Simple Inventory Database System
7
Weather Data Storage and Retrieval System
8
Customer Data Cleaning and Processing Tool
9
File-Based Data Aggregation System
10
Basic API Data Fetching and Storage System
11
Transaction Data Processing System
12
Simple Data Reporting Tool

1-on-1 Mentorship

Get personalized guidance from industry experts. Regular code reviews, career advice, and technical support throughout your internship.

Certificate

Earn an industry-recognized certificate upon successful completion. Boost your resume and stand out to potential employers.

Outcomes That Matter

Real Results for
Real Students
Real Results for
Real Students
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