Vikram Shetty
Java Developer, TCS
→ AWS Data Engineer, Swiggy
8 months
For working professionals · Online + Offline, Bengaluru
Become an AWS Data Engineer
Become an AWS Data Engineer. Live batches for working professionals in Bengaluru, with real pipeline projects and placement support until you are hired.
50+ placed/monthSupport until placedOnline + offline Bengaluru
- You end up as
- AWS Data Engineer
- Duration
- 5–6 months
- Mode
- Online + offline (Bengaluru)
- Mock interviews
- Unlimited
The AWS Data Engineering track takes working professionals from SQL and scripting to owning production pipelines on Glue, EMR and Redshift. It runs five to six months alongside a full-time job, and includes unlimited mock interviews plus placement support until you are hired.
Who this track is for
Java, Python or SQL developers and support engineers with two or more years of experience who want product-company data roles on the AWS stack.
What you will be able to do
Own the work end to end: design it, build it, explain it in an interview, and keep it running once it is live. Every module ends with something you can show a hiring manager rather than a certificate.
Outcome role and salary range
AWS Data Engineer — typically ₹14L–₹28L for engineers with 3–8 years of experience. Your actual offer depends on your experience, interview performance and the company.
AWS GlueRedshiftPySparkS3KinesisAirflow
Syllabus
5 modules over 5–6 months.
1 Foundations
- SQL for data engineering: windows, CTEs, query plans
- Python for data work: pandas, typing, testing
- Data modelling: star schema, slowly changing dimensions
- Linux, Git and the command line you actually need
2 AWS core
- S3 storage classes, partitioning and lifecycle rules
- IAM roles, policies and least-privilege access
- Glue catalog, crawlers and ETL jobs
- Lambda, EventBridge and event-driven ingestion
3 Spark and processing
- PySpark DataFrames, partitioning and shuffles
- EMR clusters and Glue Spark jobs
- Parquet, compaction and file-size tuning
- Performance tuning: skew, broadcast joins, caching
4 Warehouse and streaming
- Redshift distribution and sort keys
- Kinesis and streaming ingestion patterns
- Airflow DAGs, retries and backfills
- Data quality checks and pipeline observability
5 Production practice
- CI/CD for data pipelines
- Monitoring, alerting and on-call basics
- Interview system design: designing a pipeline end to end
- Portfolio review and architecture write-ups
Projects you build
Built on real company-style problems, and written up so you can walk an interviewer through them.
Project 1
Batch pipeline on Glue
Land raw data in S3, catalog it, transform with a Glue Spark job, and load Redshift with a repeatable backfill path.
Project 2
Streaming ingestion
Consume a Kinesis stream into partitioned S3 with schema handling and a replay strategy for bad records.
Project 3
Orchestration and recovery
Wire the pipeline into Airflow with retries, alerting and a documented runbook for the three most likely failures.
How the mock interviews work
- 1 You do a first mock early, before you feel ready. It shows you the gap between what you know and what you can say out loud under pressure.
- 2 Weekly mocks with the placement team after that, each one recorded, each one followed by written feedback on what to fix.
- 3 Rounds are modelled on the companies you are actually targeting — the technical round, the system design round and the manager round each get their own preparation.
- 4 There is no cap. Mocks continue until you are clearing real interviews, not until a quota runs out.
Placed from this track
All placements →Fee
Fee & payment
Fee depends on the track and your starting point. EMI options available. We'll walk you through it in your free assessment call — no pressure, no hidden charges.
Book your assessment →FAQ
Questions about this track
Still unsure? A counsellor can answer anything in a free 30-minute call.
Talk to usCan I do this track while working full time?
Yes. Batches run on weekday evenings and weekends, sessions are recorded, and mock interviews are scheduled around your working hours.
What do I need to know before starting the Data Engineering (AWS) track?
Two or more years of work experience and comfort with basic logic. We cover the technical foundations from the start, so you do not need prior experience with these specific tools.
How much does the Data Engineering (AWS) track cost?
It varies by track and your starting point. We share the exact fee and EMI plans in your assessment call, along with what is included.
What happens if I do not clear interviews in the first few attempts?
Mock interviews continue without a cap and placement support runs until you are hired. Most people need several attempts, and the feedback after each one is the point.
Free assessment
Your next role starts with one call.
Not sure which track fits you? Book a free 30-minute assessment.
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Talk to a real counsellor
Your profile, target role and a realistic timeline.
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Get a recommended track
Matched to where you are today, not your current title.
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Know the fee and EMI
No pressure, no hidden charges.
Free · 30-minute call
Book your free assessment
Tell us where you are today and we'll map the right track for you.