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Online + offline · Bengaluru

Data Engineering (AWS) Course: Become an AWS Data Engineer

Become an AWS Data Engineer: Glue, PySpark, S3, Redshift, EMR and Athena. Live batches in Bangalore with placement support until you are hired.

  • 15+ placed every month
  • Support until you are placed

The AWS Data Engineering track takes working professionals from Python and SQL foundations to owning production pipelines on AWS Glue, PySpark, S3, Redshift, EMR, Athena, Lambda, Kinesis and Step Functions or Airflow. It runs three months alongside a full-time job, and includes unlimited mock interviews plus placement support until you are hired.

Who this track is for

Working professionals with two or more years of experience, IT or non-IT, and people returning after a career gap.

  • IT professionals: Java, Python or SQL developers and support engineers who want product-company data roles on AWS.
  • Non-IT professionals: Analysts, MIS, finance and operations people who already work with data and want an engineering role.
  • Returning after a career gap: Engineers and analysts coming back after a break who want a current AWS stack and help explaining the gap.

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.

Syllabus

What you learn, module by module

6 modules and 24 topics over 3 months, live, alongside your job.

  1. Python for data engineering 4 topics · Core Python: data types, functions, modules and error handling, …
    • Core Python: data types, functions, modules and error handling
    • Files, JSON, CSV and Parquet; calling REST APIs
    • pandas for cleaning, joining and reshaping data
    • Linux, Git and tested pipeline code with pytest and logging
  2. SQL for data engineering 4 topics · Joins, GROUP BY, subqueries and CTEs, …
    • Joins, GROUP BY, subqueries and CTEs
    • Window functions: ranking, running totals, de-duplication
    • Reading query plans, indexes and partition pruning
    • Data modelling: star schema, slowly changing dimensions
  3. AWS data platform 4 topics · S3 storage classes, partitioning and lifecycle rules, …
    • S3 storage classes, partitioning and lifecycle rules
    • IAM roles, least-privilege policies and Lake Formation
    • Glue Data Catalog, crawlers and schema changes
    • Lambda and EventBridge for event-driven ingestion
  4. PySpark on Glue and EMR 4 topics · PySpark DataFrames, partitioning and shuffles, …
    • PySpark DataFrames, partitioning and shuffles
    • Glue Spark jobs, job bookmarks and DynamicFrames
    • EMR clusters and EMR Serverless
    • Tuning: skew, broadcast joins, Parquet file sizes
  5. Query, warehouse and streaming 4 topics · Athena over the data lake, partition projection, …
    • Athena over the data lake, partition projection
    • Redshift distribution and sort keys, COPY and Spectrum
    • Kinesis Data Streams and Firehose
    • Apache Iceberg tables on S3
  6. Orchestration and production 4 topics · Step Functions and Airflow DAGs: retries and backfills, …
    • Step Functions and Airflow DAGs: retries and backfills
    • Data quality checks and CloudWatch monitoring
    • CI/CD and infrastructure as code for pipelines
    • Interview system design and portfolio review

Portfolio

3 projects you can explain in interviews

Built on real company-style problems, and written up so you can walk an interviewer through them.

  1. Batch lake on Glue and Athena

    Land raw data in S3, catalog it, transform with a Glue PySpark job into partitioned tables, query in Athena and load Redshift with a backfill path.

  2. Streaming ingestion

    Consume a Kinesis stream with Lambda into partitioned S3, with schema handling and a replay strategy for bad records.

  3. Orchestration and recovery

    Wire the pipeline into Step Functions or Airflow with retries, alerts and a runbook for the three most likely failures.

Mock interviews

Practice until real interviews feel easy

Every mock looks like the real thing, and there is no limit on how many you get.

  1. Start early

    Your first mock comes before you feel ready. It shows the gap between what you know and what you can say under pressure.

  2. Weekly and recorded

    A mock with the placement team every week, each one recorded and followed by written feedback on what to fix.

  3. Real company rounds

    Technical, system design and manager rounds, modelled on the companies you are actually targeting.

  4. No cap

    Mocks continue until you are clearing real interviews, not until a quota runs out.

Proof

Placed from this track

All placement stories →

Vikram Shetty

Placed
  1. Before Java Developer TCS
  2. After AWS Data Engineer Swiggy

Vikram wrote Java for six years at a service company and wanted product-company data work. He learned Spark and the AWS data stack alongside his job, built three portfolio projects, and joined Swiggy as an AWS Data Engineer within three months.

Read Vikram's story

Vikram's move

Time to the new role
3months
Now working at
Swiggy

Across Stint Academy

Placed every month
15+
Working professionals, not freshers
Hiring companies
100+
MNCs, startups and product companies

Vikram's numbers are from their own placement. Results depend on your experience and interviews.

Fee

Most of your fee is paid after you're placed

  • Training + placement ₹3,50,000 incl. GST
  • Placement only ₹3,20,000 incl. GST

Not placed within 3 months? We return your ₹50,000 token. The ₹2,70,000 balance is due only if you're placed.

How the payments split

  1. Training fee ₹30,000 At enrolment · Placement only: ₹0
  2. Placement token ₹50,000 At enrolment · Refundable
  3. Placement fee balance ₹2,70,000 After you're placed · Only when placed

Both plans include GST. Pay only into the account of TMG Edutech Pvt Ltd and keep every receipt.

FAQ

Questions about this track

Straight answers to what people ask before they book a call.

Can 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.

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.

  • Talk to a real counselloryour target role and timeline

  • Get a recommended trackmatched to you, not your title

  • Know the feemost of it is paid after you’re placed

Free · 30-minute call

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