Master Program
Data Engineering
- 4.721 students
- Last updated : March 6, 2026
Overview
The Data Engineering Master Program is designed to help learners build expertise in designing, developing, and managing large-scale data systems. This program focuses on real-world data workflows using SQL, Python, cloud platforms, big data tools, ETL pipelines, data warehousing, and modern technologies like Spark, Kafka, Airflow, and dbt.
Learners develop the skills required to handle enterprise data, build scalable pipelines, manage data ecosystems, and support AI/ML systems in production environments.
Ideal for aspiring Data Engineers, Software Developers, Analysts, and anyone who wants to master data architecture and pipeline engineering.
Program Objective
- Gain mastery over data modeling, ETL/ELT workflows, and data warehousing.
- Learn how to build batch and streaming data pipelines using modern tools.
- Work with big data technologies like Hadoop, Spark, and Kafka.
- Build real-industry portfolio projects in multiple domains.
- Design scalable architectures using AWS, Azure, or GCP cloud services.
- Use Airflow/dbt for pipeline orchestration and transformation.
- Learn how to support analytics and machine learning teams with reliable data.
What You Will Learn
- SQL fundamentals (DDL, DML, joins, subqueries)
- Advanced SQL (window functions, CTEs, performance tuning)
- Python for data engineering
- Data types, data structures & file formats (CSV, Parquet, ORC, Avro)
- OLTP vs OLAP
- Schema design (Star, Snowflake)
- Dimensional modeling
- Fundamentals of Data Lakes & Lakehouse
- Warehouse tools: Snowflake, BigQuery, Redshift, Azure Synapse
- Batch processing pipelines
- Real-time data ingestion
- ETL orchestration using Airflow
- Data transformation with dbt
- Data quality checks & validation (Great Expectations)
- Hadoop ecosystem overview
- Apache Spark (RDD, DataFrame, Spark SQL, PySpark)
- Apache Kafka for real-time streaming
- Optimizing big data jobs for performance & cost efficiency
AWS: S3, Lambda, Glue, EMR, Athena, Redshift
Azure: Data Factory, Data Lake, Databricks, Synapse
GCP: BigQuery, Dataflow, Cloud Storage, Pub/Sub
You will learn how to design, build, and deploy data solutions on the cloud.
- Preparing data for ML pipelines
- Feature engineering pipelines
- Building datasets for analysts & data scientists
- Integrating with MLflow & MLOps-ready workflows
Tools & Technologies Covered
Python, SQL, Bash
Hadoop, Spark, Kafka
Airflow, dbt
AWS / Azure / GCP
Snowflake, BigQuery, Redshift
Delta Lake, Lakehouse architecture
Git, Docker, APIs, Great Expectations
Projects You Will Build
Projects are modeled after real company use-cases in e-commerce, finance, healthcare, telecom, logistics, and energy.
Career Outcomes
This Master Program prepares you for top data engineering roles:
Why Learners Choose This Program
- Comprehensive training from fundamentals to advanced level
- Curriculum aligned with real job descriptions & industry demands
- Hands-on projects with production-style data systems
- Covers both batch & streaming pipelines
- Learn cloud-native data architecture
- Build a job-ready GitHub portfolio
- Career guidance, resume preparation, and interview support
Instructor
Data Science Expert
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