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Online + offline · Bengaluru

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

Become an Azure Data Engineer: Databricks, Data Factory, PySpark, Delta Lake and Synapse. Live batches in Bangalore, placement support until hired.

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

The Azure Data Engineering track takes working professionals from Python and SQL foundations to owning production lakehouse pipelines on Azure Data Factory, Databricks, PySpark, Delta Lake, ADLS Gen2, Unity Catalog and Synapse. 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: Support, ETL, SQL and backend engineers who want to own data pipelines rather than tickets.
  • 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 Azure 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 25 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
    • Writing reusable, 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. Azure data platform 5 topics · Lakehouse design and the medallion architecture, …
    • Lakehouse design and the medallion architecture
    • ADLS Gen2: hierarchical namespace, containers, access tiers
    • Azure Data Factory: pipelines, triggers, metadata-driven loads
    • Linked services and self-hosted integration runtimes
    • Key Vault, managed identities and role-based access
  4. Databricks and PySpark 4 topics · PySpark DataFrames, partitioning and shuffles, …
    • PySpark DataFrames, partitioning and shuffles
    • Databricks clusters, notebooks and Workflows
    • Auto Loader and Structured Streaming
    • Performance tuning: skew, broadcast joins, caching
  5. Delta Lake and Unity Catalog 4 topics · Delta tables: ACID, time travel, MERGE, schema evolution, …
    • Delta tables: ACID, time travel, MERGE, schema evolution
    • OPTIMIZE, Z-ordering and VACUUM
    • Unity Catalog: catalogs, grants and lineage
    • Delta Live Tables and data quality expectations
  6. Serving and production 4 topics · Synapse serverless and dedicated SQL pools, …
    • Synapse serverless and dedicated SQL pools
    • Incremental loads and change data capture
    • CI/CD with Azure DevOps, monitoring and cost control
    • 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. Metadata-driven ingestion with ADF

    Pull from a transactional source into ADLS Gen2 with watermarks, handle late-arriving records, and prove reruns are idempotent.

  2. Databricks medallion lakehouse

    Build bronze, silver and gold Delta tables in PySpark with Auto Loader, quality gates and Unity Catalog permissions.

  3. Serving layer and cost review

    Serve the gold layer through Synapse for reporting, then cut its running cost and write up what you changed and why.

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 →

Arun Kumar

Placed
  1. Before Support Engineer Infosys
  2. After Azure Data Engineer Maersk

Arun spent four years in production support and could not get past resume screens for data roles. He rebuilt his profile around two Azure pipeline projects, did eleven mock interviews with our placement team, and joined Maersk as an Azure Data Engineer three months after his assessment call.

Read Arun's story

Arun's move

Time to the new role
3months
Salary, per year
₹6.5L → ₹18L

Across Stint Academy

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

Arun'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 (Azure) 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

Book your free assessment

Tell us where you are today and we'll map the right track for you.