Data engineers focus on building and maintaining systems that allow data collection, storage, and analysis. They are responsible for creating data pipelines, ensuring data is clean, and enabling data scientists or analysts to access and work with the data. They use tools like Apache Hadoop, Apache Spark, and various database systems (SQL/NoSQL) to build the infrastructure that supports data analysis and processing.
Get hands-on experience with industry tools & technologies.
Career Growth
Progress from junior to senior data engineering roles.
Competitive Edge
Gain expertise in cloud, ML, and real-time processing.
Job Market Ready
Build a portfolio of projects to showcase technical abilities.
Course Objectives
Develop Industry-Ready Data Engineering Skills
Equip learners with hands-on experience in SQL, NoSQL, Python, ETL frameworks, and cloud databases to prepare them for real-world data engineering roles.
Design, Build, and Optimize Scalable Data Pipelines
Enable students to architect and implement batch and real-time data pipelines using tools like Apache Airflow, Spark, Kafka, and cloud-based ETL solutions while ensuring performance optimization.
Master Data Storage, Processing, and Governance
Teach students how to work with Data Warehouses, Data Lakes, and NoSQL systems, ensuring data quality, security, and governance in compliance with industry standards.
Master Real-World Data Engineering Skills
Fast — Tools, Pipelines, Cloud, and
Confidence.
12 Hrs
.2 Weeks
0.5 Months
Associate Level Course (Data Engineering)
To build a strong foundation in the core principles of data engineering, focusing on data management, processing, and storage. This level aims to introduce essential tools, techniques, and practices required to step into the field of data engineering and prepare learners for advanced technical exploration.
Core Areas of Study
Data Engineering Overview
Database Basics
Data Formats & Storage
ETL/ELT Basics
Python for Data Engineering
Data Warehousing
Artificial Intelligence
The Associate level program also includes a Career Excellence Program, designed to help participants develop communication skills, build confidence, strengthen their professional presence, and align their capabilities with long-term career success.
- Prerequisite -
A University Graduate or Certificate level + 2 Yrs Internship
To equip learners with hands-on expertise in real-time data processing, cloud-native engineering, data governance, and observability. Learners are trained to design, orchestrate, and manage end-to-end data pipelines across hybrid and cloud environments while ensuring performance, scalability, and compliance.
Core Areas of Study
Crash Course on Associate Level
Two to three classes of the Associate level course to lay the foundation for the Professional level program
Pipeline Orchestration (PO)
Cloud Platforms (AWS, GCP, Azure) (CP)
Data Lakes & Lakehouses (DLH)
Advanced SQL (ASQL)
Real-time Processing (RTP)
Cloud-Native Engineering (CNE)
Governance & Security (GS)
Monitoring & Observability (MO)
Capstone Project: Apply all concepts in a real-world project covering batch + stream processing, cloud deployment, and documentation
The Professional level program also includes a Career Development Program, which aims to elevate professional presence by improving verbal, non-verbal, and written communication, activating influence, and strengthening decision-making, collaboration, and proactive contribution in real-world work settings, while applying a leadership mindset, trust-building, and team execution in high-pressure simulations
- Prerequisite -
A University Graduate + Work Experience
This crash course offers a fast-paced, hands-on introduction to modern data engineering.
In just six sessions, learners will gain practical exposure to key tools, concepts, and workflows
used in real-world data pipelines. Designed for aspiring data professionals, this course bridges
the gap between theory and implementation.
Hands-on Skill Building – Practical experience with real tools, code, and cloud environments.
Accelerated Learning Path – Compressed timeline tailored for professionals and fast learners.
Industry-Relevant Exposure – Aligned with current job market needs in data engineering roles.
Career Transition Ready – Equips participants with the confidence and language to pursue data engineering roles or projects.
Foundations of Data Engineering
Understand data engineering roles, data lifecycle, and the modern data ecosystem. Working with Databases
Learn SQL basics, relational vs NoSQL concepts, and data modeling essentials. Data Formats & Processing
Handle CSV, JSON, Parquet, and compressed data formats using Python and CLI.ETL Pipeline Design
Build lightweight pipelines: extract from APIs, transform with Python, and load into databases.
Scheduling & Automation
Automate tasks with Cron and understand basic pipeline orchestration logic. Cloud & Warehousing Essentials
Get introduced to Snowflake, BigQuery, and Redshift, with practical use cases.
