AWS Data Engineering Training & Certification Course (72 Hours Master Program)
AWS Data Engineering Training: The AWS Data Engineering Training programme by SkilBrill is a comprehensive 12-week (72-hour) hands-on program covering AWS S3, AWS Glue, Amazon Redshift, Amazon EMR, AWS Lambda, Amazon Kinesis, Athena, Step Functions. Available in classroom (Chennai Thoraipakkam), live online, and hybrid modes across India.
Every organisation needs reliable data pipelines to compete.
✓Data engineers moving to AWS: Join this programme to move into AWS Data Engineering Training roles.
✓Cloud aspirants: Join this programme to move into AWS Data Engineering Training roles.
✓ETL developers: Join this programme to move into AWS Data Engineering Training roles.
✓Big data professionals: Join this programme to move into AWS Data Engineering Training roles.
Fee: ₹15,000 – ₹75,000100% Practical Labs
72-Hour Syllabus
12-Module AWS Data Engineering Curriculum
AWS Data Engineering Training is a 12-week (72-hour) SkilBrill programme covering AWS S3, AWS Glue, Amazon Redshift, Amazon EMR. Delivery is classroom, live online and hybrid. The 12 modules below are the full syllabus — about 6 hours each, with labs. Completing them prepares you for AWS DEA-C01 and placement support.
1
Foundations
M1: AWS Data Engineering Foundations
AWS Global Infrastructure and IAM Fundamentals, Data Engineering Roles and Modern Data Architecture, AWS An…
2
Foundations
M2: SQL and Python for Data Engineering
Advanced SQL for Analytics and ETL, Python for Data Processing: pandas, boto3, psycopg2, Working with JSON,…
3
Foundations
M3: Amazon S3 and Data Lake
S3 Buckets, Objects, Versioning, and Lifecycle Policies, S3 Security: Bucket Policies, ACLs, and Encryption…
4
Core Skills
M4: AWS Glue and ETL Development
Glue Data Catalog, Crawlers, and Classifiers, Glue ETL Jobs with PySpark and Spark, Glue Studio Visual ETL
5
Core Skills
M5: Amazon Redshift Data Warehouse
Redshift Architecture, Distribution Styles, and Sort Keys, Loading Data from S3, Glue, and DMS, Query Optim…
6
Platform
M6: Streaming Data with Kinesis and MSK
Kinesis Data Streams, Firehose, and Analytics, Apache Kafka on AWS with MSK, Stream Processing with Lambda…
7
Platform
M7: AWS Lambda and Event-Driven Processing
Lambda Functions for Data Processing, Event Sources: S3, SQS, SNS, EventBridge, Kinesis, Step Functions for…
8
Governance
M8: Data Orchestration with MWAA and Step Functions
AWS Lake Formation and Fine-Grained Access Control, Data Quality with Deequ and Great Expectations, Encrypt…
10
Advanced
M10: Big Data with EMR and Spark
EMR Architecture, Clusters, and Notebooks, Spark on EMR: DataFrames, SQL, Streaming, Hadoop Ecosystem on EMR
11
Advanced
M11: DevOps, CI/CD and Monitoring
Infrastructure as Code with CloudFormation and Terraform, CI/CD for Data Pipelines with CodePipeline, Cloud…
12
Capstone
M12: Real-World Projects and Interview Prep
E-Commerce Data Lake and Analytics Pipeline, Real-Time Clickstream Analytics with Kinesis, Data Warehouse M…
Frequently Asked Questions
AWS Data Engineering Training at SkilBrill is a live programme with labs, a completion certificate and placement support. The questions below cover eligibility, duration, tools, projects and how to enrol.
What is AWS Data Engineering?
AWS Data Engineering is a practical, job-oriented programme at SkilBrill. This training covers the core concepts, industry-standard tools, hands-on labs and real-world projects that employers look for when hiring for AWS Data Engineering roles. This programme covers the concepts, tools and real-world implementations needed for job roles.
Who should learn AWS Data Engineering?
Professionals and fresh graduates looking to build a career in Data Engineering and related roles.
What are the prerequisites?
Basic SQL and Python
What topics are covered?
Core concepts, architecture, tools, hands-on labs, projects, interview preparation and career support.
Is training online or classroom?
The master programme is online. City pages offer classroom and hybrid options where supported.
How long is the AWS Data Engineering Training course at SkilBrill?
SkilBrill's AWS Data Engineering Training programme runs for 12 weeks with 3 sessions per week (2 hours each), totalling 72 classroom hours across 12 modules. Weekday, weekend and evening batches are available.
Why should I learn AWS Data Engineering Training at SkilBrill?
SkilBrill Training Institute in Thoraipakkam, Chennai delivers AWS Data Engineering Training with working-professional trainers, production-style labs and placement support. The programme is 12 weeks (72 classroom hours) with weekday, weekend and evening batches in classroom, live online and hybrid modes.
How does SkilBrill help with jobs after AWS Data Engineering Training?
Beyond classroom hours, SkilBrill coaches you on a AWS Data Engineering Training resume, LinkedIn profile, mock interviews and project walkthroughs, then connects eligible candidates with hiring partners. Ask admissions for the current placement workflow on +91 8610964691.
How do I enroll in AWS Data Engineering Training at SkilBrill?
Use the Enroll form on this page, call +91 8610964691, or WhatsApp 918610964691. Share your name, email and preferred mode (online, classroom or one-to-one) and a counsellor will confirm the next AWS Data Engineering Training batch. Fees currently range ₹15,000 – ₹75,000.
Where is SkilBrill Training Institute located?
SkilBrill is at No. 22, 200 Feet Radial Road, Thoraipakkam, Chennai 600097, on the OMR IT corridor, about five minutes from Thoraipakkam Metro. Classroom batches for AWS Data Engineering Training run here; online learners join the same faculty remotely.
AWS has over 200 services. You do not need to know all of them. You need to know which 20 matter for your role and how they fit together.
Start with the global infrastructure and IAM. Everything else builds on those foundations.
For data engineering, focus on S3, Glue, Redshift, EMR, and Kinesis. Skip the certification exams until you have built something real.
The free tier is your lab. Build a data pipeline, break it, fix it. That is how you learn.
Structured training cuts the learning curve from 18 months to 12 weeks.
Join SkilBrill's AWS Data Engineering Training — a comprehensive programme covering the tools, concepts and real-world projects that employers look for. The master course is delivered through live online sessions with weekday, weekend and evening batch options.
Course Overview
AWS Data Engineering is one of the most in-demand cloud specialisations. This programme covers S3, Glue, Redshift, Kinesis, Lambda, and the full AWS analytics stack through hands-on labs and projects.
Why Learn AWS Data Engineering
AWS holds the largest cloud market share. Learning AWS data services opens roles in cloud data engineering, analytics engineering, and data platform teams.
Hands-on lab on AWS Data Lake Fundamentals — build and run a working example covering S3 buckets and partitioning, Data formats, Lakehouse concepts.
Hands-on lab on ETL with AWS Glue — build and run a working example covering Glue crawlers and catalog, Glue jobs with PySpark, Job bookmarks.
Hands-on lab on Data Warehousing with Redshift — build and run a working example covering Spectrum and RA3 nodes, Workload management, Query tuning.
Hands-on lab on Serverless Data Processing — build and run a working example covering AWS Lambda, Athena federated queries, Step Functions.
Hands-on lab on Streaming and Real-Time — build and run a working example covering Amazon Kinesis, Lambda event processing, Real-time dashboards.
Hands-on lab on Pipeline Orchestration and Security — build and run a working example covering IAM roles, VPC endpoints, Monitoring with CloudWatch.
Career Roadmap
Role Progression
AWS Data Engineer
Cloud Data Engineer
Data Pipeline Engineer
Big Data Engineer
Step-by-Step Learning Path
Master AWS Data Lake Fundamentals
Master ETL with AWS Glue
Master Data Warehousing with Redshift
Master Serverless Data Processing
Master Streaming and Real-Time
Master Pipeline Orchestration and Security
Prepare for technical interviews and update your resume and portfolio
Course Duration, Mode and Certification
The course runs for 12 weeks with 3 sessions per week (2 hours per session), totalling 72 classroom hours. Classroom, online, and hybrid modes are available where supported. You will receive a SkilBrill completion certificate and work on real-world projects, with dedicated interview preparation support at the end of the programme.
Why Train at SkilBrill
SkilBrill Training Institute focuses on making you employable. Trainers are working professionals, labs mirror real production environments, and the placement cell connects eligible candidates with hiring companies. Enquire today for the next batch start date and fee details.
Career and Job Support
SkilBrill provides practical career support to help you move from training into relevant roles. Services include:
Resume assistance
LinkedIn profile guidance
Portfolio guidance
Technical interview preparation
Mock interviews
Project explanation preparation
Job-search guidance
Placement assistance
Resume Building Guidance
Keep your resume to one page if you have less than five years of experience.
Use a clean format with clear sections: contact, objective, skills, projects, education and certifications.
Tailor your skills section to match the job description.
Quantify achievements where possible, for example "reduced regression time by 30%".
Include links to your GitHub, LinkedIn and portfolio if available.
Proofread carefully; spelling and grammar errors create a poor impression.
LinkedIn Profile Optimisation
Use a professional headshot and a headline that includes your target role.
Write a summary that highlights your skills, projects and career goals.
List relevant tools, frameworks and certifications in the Skills section.
Share posts or articles about what you are learning to show activity.
Connect with trainers, classmates and professionals in your target domain.
Request recommendations from mentors or project reviewers.
GitHub and Portfolio Guidance
Create well-named repositories for each major project.
Add a README file explaining the project, technologies, setup steps and screenshots.
Use meaningful commit messages and keep code organised.
Include a portfolio website or GitHub profile readme that links to your best work.
Keep sensitive data like passwords and API keys out of public repositories.
Regularly update repositories with improvements and new projects.
Job Search Strategy
Update your resume and LinkedIn profile before applying.
Apply to roles on LinkedIn, Naukri, Indeed and company career pages.
Customise each application to match the job description.
Prepare a short elevator pitch for phone screenings.
Practise technical and behavioural questions daily.
Follow up politely after interviews and ask for feedback.
Attend meetups, webinars and networking events in your domain.
Frequently Asked Questions
What is AWS Data Engineering?
AWS Data Engineering is a practical, job-oriented programme at SkilBrill. This training covers the core concepts, industry-standard tools, hands-on labs and real-world projects that employers look for when hiring for AWS Data Engineering roles. This programme covers the concepts, tools and real-world implementations needed for job roles.
Who should learn AWS Data Engineering?
Professionals and fresh graduates looking to build a career in Data Engineering and related roles.
What are the prerequisites?
Basic SQL and Python
What topics are covered?
Core concepts, architecture, tools, hands-on labs, projects, interview preparation and career support.
Is training online or classroom?
The master programme is online. City pages offer classroom and hybrid options where supported.
SkilBrill’s AWS Data Engineering Training is a 12 weeks, 72-hour job-oriented programme covering AWS Glue, Redshift, EMR with placement support. Classroom, live online and hybrid modes. Call +91 8610964691 to enrol.
Real-World Projects
Capstone Project
Bronze/silver/gold lake on S3 with Glue jobs, Redshift Spectrum and a Kinesis stream, deployed via IaC.
Production Scenario
A Glue job blows the DPU budget. Partition, compact small files and cut cost 40%.
