Data Engineering
Become a job-ready Data Engineer with Python, SQL, data warehousing, ETL/ELT pipelines, Apache Spark, Airflow, Kafka, dbt, and cloud data platforms on AWS & GCP. Learn to build the reliable, scalable data pipelines that power analytics and AI, and ship real, portfolio-worthy data platform projects from raw source to production. Start free, go job-ready, and launch your career in data.
In short: This Data Engineering program is a 60–65 hour, fully online, job-ready course. You start with 15 hours of free foundations (Python, SQL, Linux & shell, Git) and continue into a 45–50 hour paid program covering advanced SQL, data modeling and warehousing, ETL/ELT pipelines, big data with Apache Spark, orchestration with Airflow, streaming with Kafka, analytics engineering with dbt, cloud data platforms on AWS and GCP, and Docker & CI/CD — finishing with an end-to-end capstone data platform, a portfolio, and full interview prep.
The Data Engineering course is designed to help learners master the complete data pipeline lifecycle — from raw source systems to clean, reliable, analytics-ready data at scale — and become immediately employable as Data Engineers, Analytics Engineers, and ETL Developers.
This course covers Python for data engineering, advanced SQL and analytical query engineering, data modeling and data warehousing, batch ETL/ELT pipeline design, big data processing with Apache Spark, workflow orchestration with Apache Airflow, real-time streaming with Apache Kafka, analytics engineering with dbt and the modern data stack, cloud data platforms and data lakes on AWS and GCP, and deploying pipelines with Docker and CI/CD. Learners build real, portfolio-worthy data platform projects at every stage using hands-on, project-based learning.
By completing this training, participants will be well-prepared for Data Engineer, Analytics Engineer, and ETL Developer roles, with a polished portfolio, a strong resume, and interview readiness.
Course Highlights
- End-to-end coverage from Python & SQL foundations to production cloud data platforms
- Design data models and build dimensional data warehouses
- Build batch ETL/ELT pipelines and process big data with Apache Spark
- Orchestrate workflows with Airflow and stream data with Kafka
- Cloud data lakes & warehouses on AWS and GCP, deployed with Docker & CI/CD
- Job-readiness: portfolio, resume, and interview prep
Learning Outcomes
- Write efficient Python and advanced SQL for data engineering workloads
- Design data models, schemas, and data warehouses for analytics
- Build reliable batch ETL/ELT pipelines and process big data with Spark
- Orchestrate pipelines with Airflow and build streaming pipelines with Kafka
- Build data lakes and warehouses on AWS and GCP
- Deploy and monitor data pipelines with Docker and CI/CD
Job Opportunities
- Data Engineer
- Analytics Engineer
- ETL / Data Pipeline Developer
- Big Data Engineer
- Data Platform Engineer
- Cloud Data Engineer
What You'll Build (Portfolio Projects)
- A Python ETL pipeline that ingests, cleans, and loads real data
- A dimensional data warehouse with a star-schema model
- A big-data processing job on Apache Spark
- An Airflow-orchestrated pipeline and a real-time Kafka streaming pipeline
- A deployed end-to-end cloud data platform capstone
Tools & Skills Covered
- Python, SQL, Apache Spark
- Apache Airflow, Apache Kafka, dbt
- Snowflake / BigQuery / Redshift, AWS, GCP, Docker, Git
Material Includes
- Class recordings
- Datasets & project starter files
- Certificate
Who Can Join
- IT Graduates & Developers
- Aspiring Data & Analytics Engineers
- Career Switchers
Related Courses
Free Foundation — 15 Hours
Start learning for free. Build your core Python, SQL, and Linux foundations before advancing to the job-ready Data Engineering program.
Estimated Time: 5 Hours
Learning Objectives:- Write and run Python programs confidently
- Use core programming logic to prepare for data pipelines
- Python setup, syntax, variables, and data types
- Conditionals, loops, functions, and modules
- Lists, dictionaries, and file handling for data processing
- Build Python scripts that read, clean, and transform data files
Estimated Time: 4 Hours
Learning Objectives:- Query relational databases with confidence
- Understand how data is stored and related
- SELECT, WHERE, GROUP BY, and ORDER BY
- Joins, keys, and relationships
- Aggregations, subqueries, and table design basics
- Write SQL queries against a real relational database
Estimated Time: 4 Hours
Learning Objectives:- Work comfortably in the Linux command line
- Handle the file formats data engineers use daily
- Linux navigation, permissions, and shell scripting
- Automating tasks with cron and shell pipelines
- Working with CSV, JSON, Parquet, and APIs
- Automate a data-download and preprocessing task with a shell script
Estimated Time: 2 Hours
Learning Objectives:- Track pipeline code and configs with Git
- Collaborate and showcase work on GitHub
- Repositories, commits, and branches
- Merging, pull requests, and conflict resolution
- Building a professional data engineering GitHub profile
- Push your first pipeline projects to GitHub
Paid Job-Ready Program — 45–50 Hours
Go from foundations to fully employable. Master advanced SQL, data modeling and warehousing, ETL/ELT, Apache Spark, Airflow, Kafka, dbt, cloud data platforms, and CI/CD — everything you need to land your first data engineering job.
Estimated Time: 5 Hours
Learning Objectives:- Write advanced, performant analytical SQL
- Optimize queries for large datasets
- Window functions and common table expressions (CTEs)
- Advanced joins, set operations, and pivoting
- Indexing, query plans, and performance tuning
- Optimize a slow analytical query on a large table
Estimated Time: 5 Hours
Learning Objectives:- Design data models for analytics at scale
- Build and query modern data warehouses
- Normalization, star and snowflake schemas
- Fact and dimension tables, slowly changing dimensions
- Cloud warehouses: Snowflake, BigQuery, and Redshift
- Design and load a dimensional data warehouse
Estimated Time: 5 Hours
Learning Objectives:- Design reliable ETL and ELT data pipelines
- Ingest, transform, and load data from many sources
- ETL vs ELT patterns and pipeline architecture
- Extracting from APIs, files, and databases
- Transformations, idempotency, and data validation
- Build a Python ETL pipeline from source to warehouse
Estimated Time: 6 Hours
Learning Objectives:- Process large-scale data with distributed computing
- Write efficient Spark transformations
- Spark architecture, RDDs, and DataFrames
- PySpark transformations, actions, and Spark SQL
- Partitioning, caching, and performance optimization
- Process a multi-gigabyte dataset with PySpark
Estimated Time: 5 Hours
Learning Objectives:- Schedule and orchestrate complex data pipelines
- Monitor, retry, and manage pipeline dependencies
- DAGs, tasks, operators, and scheduling
- Dependencies, retries, backfills, and SLAs
- Monitoring, logging, and alerting
- Orchestrate a multi-step ETL pipeline with Airflow
Estimated Time: 4 Hours
Learning Objectives:- Build real-time data pipelines
- Understand event-driven data architecture
- Kafka topics, producers, consumers, and partitions
- Stream processing and delivery guarantees
- Batch vs streaming trade-offs
- Build a real-time streaming pipeline with Kafka
Estimated Time: 5 Hours
Learning Objectives:- Build data lakes and pipelines in the cloud
- Use managed data services on AWS and GCP
- Data lakes on S3 / Google Cloud Storage
- AWS Glue, Athena, EMR and GCP Dataflow, BigQuery
- Lakehouse architecture and file formats (Parquet, Delta)
- Build a cloud data lake and query it serverlessly
Estimated Time: 4 Hours
Learning Objectives:- Transform warehouse data with dbt
- Apply software engineering practices to analytics
- dbt models, sources, refs, and materializations
- Testing, documentation, and data quality
- The modern data stack and ELT workflow
- Build and test a dbt transformation project
Estimated Time: 5 Hours
Learning Objectives:- Containerize and deploy data pipelines
- Automate testing and delivery with CI/CD
- Docker images, containers, and Docker Compose
- CI/CD pipelines with GitHub Actions
- Data quality monitoring, logging, and observability
- Containerize a pipeline and deploy it via CI/CD
Estimated Time: 4 Hours
Learning Objectives:- Ship a complete, portfolio-ready data platform
- Prepare for the data engineering job search
- End-to-end capstone: ingestion, warehouse, orchestration
- Portfolio, GitHub, and resume optimization
- Data engineering interview, SQL, and system-design prep
- Build and present an end-to-end cloud data platform
- Mock interviews and resume review