About Course
Join SkilBrill's AWS Data Engineering Training — a practical, hands-on programme designed for learners in Delhi and across Delhi. The course is delivered through online, classroom and hybrid 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.
AWS Data Engineering Training in Delhi
Learners from Delhi can attend online, classroom and hybrid sessions led by experienced instructors. The curriculum is the same across all locations, so you receive consistent, industry-relevant training whether you join from Delhi or any other city.
Who Should Join This Course
- Data engineers moving to AWS
- Cloud aspirants
- ETL developers
- Big data professionals
Prerequisites
- Basic SQL and Python
- Understanding of databases
- Cloud fundamentals helpful
Learning Objectives
- Design data lakes on S3
- Build ETL pipelines with Glue
- Query data with Athena and Redshift
- Stream data with Kinesis
- Orchestrate pipelines with Step Functions
84 hrs across 12 modules (108 topics)
Tools and Technologies
AWS S3, AWS Glue, Amazon Redshift, Amazon EMR, AWS Lambda, Amazon Kinesis, Athena, Step Functions, IAM, CloudFormation, Python, Spark
Career Opportunities After This Programme
- AWS Data Engineer
- Cloud Data Engineer
- Data Pipeline Engineer
- Big Data Engineer
Hands-on Labs
- 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.
What career support is available?
Resume help, LinkedIn guidance, mock interviews, project explanation, and placement assistance.
Is classroom training available in Delhi?
No. SkilBrill's classroom runs only in Chennai. Learners in Delhi join the same live online / hybrid cohort taught by the Chennai faculty, with proctored labs and the same placement support.
Which companies hire for these skills in Delhi?
Learners in Delhi typically target a mix of MNCs and startups such as HCL, TCS, IBM. The programme's placement support applies equally to Delhi learners.