Data Engineering/Data Engineer

Build the Data Highways That Power Analytics & AI

This 12316 week track is for graduates and career switchers who want to build pipelines, warehouses, and platforms. Perfect for BSc, BCom, BCA, BE, BTech graduates, freshers with backend interest, or professionals transitioning from testing, support, or sysadmin roles.

Tools & Technologies

Master the complete data engineering stack from programming to cloud platforms. Build expertise in modern tools that power enterprise data infrastructure.

Python for ETL, SQL, and basic Bash scripting for automation.

SQL Server, PostgreSQL, MySQL, and NoSQL with MongoDB basics.

Star/Snowflake schema and dimensional modeling techniques.

SSIS, Azure Data Factory, AWS Glue, and Apache Airflow.

Apache Spark (PySpark), Kafka basics, and streaming processing.

Azure/AWS: S3, EC2, Databricks, Redshift, Synapse, Data Lakes.

$199

Course Duration

12-16 weeks part-time

Learning Style

Hands-on, project-based

Prerequisites

None 3 beginners welcome

12-16 Week Progress Timeline

A structured, phase-wise approach to mastering data engineering from foundations to production-ready pipelines.

Phase 1: Foundations (Weeks 133)

Database concepts: OLTP vs OLAP. SQL from refresher to advanced: joins, aggregation, window functions. Python for data scripts and data modeling basics. Mini-task: build queries and views for reporting layer.

Phase 2: ETL & Warehousing (Weeks 436)

ETL concepts: extract, transform, load, incremental loads. Build ETL workflows with SSIS/ADF/Glue. Design star schema data warehouse for Retail/Banking. Mini-project: end-to-end batch pipeline from OLTP DB to DWH.

Phase 3: Big Data & Cloud (Weeks 7310)

Intro to Big Data and Spark. Write PySpark jobs for large datasets. Streaming basics with Kafka. Cloud fundamentals: storage, compute, security. Airflow/ADF pipelines: orchestration, dependencies, retries. Mini-project: API/file ingestion to cloud to Spark to DWH.

Phase 4: Production & Capstone (Weeks 11316)

Best practices: partitioning, file formats (Parquet/ORC), optimization. Data quality checks, error handling, logging. CI/CD basics and environment separation. Capstone project: design and build a full modern data platform with documentation.

Project Building Portfolio

Build at least 3 strong projects that demonstrate real-world data engineering skills for your portfolio and interviews.

Retail Sales Data Warehouse

Design star schema (FactSales, DimCustomer, DimProduct). Build ETL to populate DWH from raw CSV/OLTP DB. Schedule daily runs and handle incremental loads.

Streaming Pipeline

Simulate real-time events (clicks, sensor readings). Ingest via Kafka-like stream. Use Spark streaming/batch to process and store in data lake.

Cloud Data Lakehouse

Use AWS/Azure stack (S3/Data Lake + Spark/Databricks + DWH). Build curated Gold layer for BI tools (Power BI/Tableau). Capstone combines all elements into single architecture.

Interview Preparation & Mentorship

Technical Preparation

SQL + scenario-based questions
SQL query practice (real interview style)
Spark, pipelines, partitioning, error handling
Cloud services Q&A (storage, compute, security)

System Design

Learn to design data pipelines for real use cases. How to ingest data from multiple sources, ensure quality, and expose to BI tools.

Profile & Communication

Resume optimization as Data Engineer/ETL Developer/Cloud Data Engineer. LinkedIn headline and summary aligned to dataplatform roles. GitHub with sample pipeline code, SQL scripts, and architecture diagrams.

Mock Interviews

1:1 technical rounds focusing on SQL and pipelines. HR and communication rounds for clarity and confidence building.

Internship & Placement Support

Internship Certificate

Based on real or simulated live projects to strengthen your resume and demonstrate practical experience.

Unlimited Interview Calls

Continuous support for relevant data engineering roles across product companies, consulting firms, startups, and cloud providers.

6 Months Post-Course Support

Dedicated guidance for targeting big IT services, MNCs, and specialized data platform roles after course completion.

Career Designations After Placement

Typical roles for freshers and professionals with 032 years of experience in data engineering.

Package Range & Market Scope

Data Engineering offers exceptional career growth with strong salary potential and explosive market demand driven by AI and cloud adoption.

₹4-20L Salary Range India

Entry-level (032 years): ¹438 LPA. Average: ¹9.6 LPA. Top performers: ¹18+ LPA depending on skills and company

$175B Global Market 2030

Data engineering market projected to grow from $29.1B (2023) to $175B by 2030.

100%+ Job Growth

Data Engineers worldwide expected to grow from 0.5M (2025) to 1.25M by 2030.

Data Engineering is not being replaced by AI – it is being boosted by it. More AI means more data, more pipelines, and more demand for skilled Data Engineers. Advanced engineers building AI-scale data ecosystems could be among the best-paid tech roles by 2030.

Get Started

AI is the Future. Be a Part of It!

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