PGPDE - Praxis
Program Objective

The Data Engineering program at Praxis, in association with Genpact, Chubb and LatentView Analytics as Knowledge Partners, is designed to create job-ready data professionals.

The program equips students with the know-how of existing tools and technologies for Data Management and Data Modelling and introduces them to the paradigms of Distributed Systems and Cloud Computing. The participants get to work on a Capstone Project that requires them to migrate data to Big-Data platforms and manage the system on the Cloud.

The overarching objective of the program is to equip students with the tools and techniques that enable them to get their first job in the exciting domain of Data Engineering. The program has been designed to offer working professionals the opportunity to upskill themselves without giving up their existing assignments.

Learning Outcomes

On successful completion of the course, students will learn how to:

Re-engineer Enterprise Data Architecture

Work with relational and NoSQL data models

Create scalable and efficient data warehouses

Work efficiently with massive datasets

Build and interact with a Cloud-based Data Warehouse

Automate and monitor data pipelines

Develop proficiency in Stream Processing using Cloud Data Lake

Solve appropriate use cases using Big Data technologies


The fastest growing tech career in the world

Data engineering is the process of transforming raw data into valuable information.

It’s a critical process for businesses that want to make data-driven decisions and is assuming importance with the generation of massive volumes of data in our daily lives.

Data engineers are professionals skilled in the collection, storing and parsing of data and utilizing machine learning to analyze the data.

Their job requires a critical understanding of both software development tools as well as business skills required to convert that data into valuable information.

Data Engineering has emerged as a top career choice in today’s data driven world. The supply demand gap in this rapidly growing sector is galloping, creating excellent opportunities for people with the right skills.

Data Engineering Market Size

  • India: $10.6 billion; to grow 4 times to $42.3 billion by 2025
  • Global: $29 billion; to grow to $106 billion by 2025

1. Data Engineer: A data engineer lays down the foundation for data management systems to ingest, integrate and maintain all the data sources. The person has knowledge of databases and understands the needs of the business and its long-time data scalability needs. Tools: SQL, XML, Hive, Pig, Spark, etc.

2. Database Administrator: A database administrator has extensive knowledge of traditional as well as new-age NoSQL and Cloud databases and ensures that the data generating and the data ingesting systems are up and running in a live business scenario.

3. Enterprise Data Architect: The enterprise data architect is responsible for visualizing and designing an organization’s enterprise data management framework that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control and purge data. They have extensive knowledge of database tools, languages like Python, Java and Scala and distributed systems like Hadoop.

4. ETL Engineer: The ETL engineer is responsible for maintaining the veracity of the data in the source and target systems. They ensure that the right kind of tools, permission and system pipelines are in place for smooth transfer of the data.


300+ hours of intensive online learning

Program co-created with Genpact, LatentView Analytics and CHUBB

Exciting placement opportunities for all candidates

Membership to the Praxis Global Alumni Network

Program Coverage
Selection Process
Online Fee Payment

The Praxis Placement Program is a structured process committed to creating quality placement opportunities for all enrolled students. It has had a consistently high placement record.

The placement team has tie-ups with prospective recruiters for the Data Engineering Program.

On an average, for every data scientist in the team, an organisation requires 4-5 data engineers. Thus, there are plenty of exciting jobs available if you have data engineering skills.

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