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Become a production Data Engineer in 16 weeks.

Ship real pipelines with Python, SQL, Airflow, Spark and Kafka. Build a modern lakehouse with dbt on Snowflake / BigQuery. Mentored by engineers who run petabyte-scale platforms.
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0% EMI availablePlacement guaranteeHands-on labs included
Data Engineer Career Track
Only 5 seats left
  • 16 weeks · live mentor-led
  • 80+ hands-on data pipeline labs
  • 3 capstone pipelines + GitHub review
  • Weekly 1:1 with your assigned mentor
  • Snowflake / BigQuery / Databricks exposure
  • Lifetime placement support
    • Book free counselling call
    30-min call · no pressure · get a personal roadmap
    16 wks
    Cohort length
    80+
    Live labs
    9.5/10
    Alumni rating
    93%
    Placement rate
    Alumni working at
    DNMPramataResponsiveSocial Bytes Pvt LtdUPLDMG
    Outcomes

    What you'll be able to do on day one at your new job

    Every module ends with a graded lab that mirrors what teams actually ship. No slides-only theory.

    Write production Python with OOP, type hints, unit tests and packaging.

    Model dimensional warehouses — Star vs Snowflake schemas — with proper grain.

    Build reliable ETL/ELT pipelines orchestrated with Airflow.

    Process TB-scale batch data with PySpark on Databricks / EMR.

    Stream events end-to-end with Kafka topics, consumers and stream processors.

    Build a lakehouse on Delta Lake / Iceberg / Hudi with ACID guarantees.

    Version pipelines with Git, containerize with Docker, deploy via CI/CD.

    Enforce data quality with Great Expectations / dbt tests.

    16-week curriculum

    Eight modules. Real data-platform work end-to-end.

    Get the detailed syllabus PDF
    1. Module 01
      Week 1

      Foundations of Data Engineering

      Set up the environment every DE team expects.

      • Data Engineer role & responsibilities
      • Linux & command line essentials
      • Git & GitHub workflows
      • Python virtualenvs & packaging
    2. Module 02
      Weeks 2–3

      Advanced Programming for Data (Python & SQL)

      The two languages you'll live in every day.

      • OOP & Python design patterns
      • CSV, JSON, Parquet, Avro handling
      • Pandas & NumPy for transformation
      • Complex joins, CTEs, subqueries
      • Window functions, indexing, EXPLAIN plans
      • PostgreSQL / MySQL depth
    3. Module 03
      Weeks 4–5

      Data Modeling & Warehousing

      Model data so analysts can trust it.

      • OLTP vs OLAP fundamentals
      • ER diagrams & normalization
      • Star vs Snowflake dimensional modeling
      • Fact & dimension tables
      • Snowflake / BigQuery / Redshift architecture
      • Columnar storage & partitioning
    4. Module 04
      Weeks 6–7

      ETL/ELT Pipeline Development & Orchestration

      Build pipelines that run every night without fail.

      • ETL vs ELT patterns
      • REST API ingestion & web scraping
      • Database dumping strategies
      • Apache Airflow DAGs & operators
      • Dependency management & retries
      • dbt models & tests
    5. Module 05
      Weeks 8–10

      Big Data Processing (Distributed Computing)

      Process TB / PB scale data reliably.

      • 5 V's of Big Data
      • Hadoop HDFS & MapReduce concepts
      • Apache Spark: RDDs, DataFrames, SQL
      • PySpark on Databricks / EMR
      • Partitioning, shuffling & optimization
    6. Module 06
      Weeks 11–12

      Stream Processing (Real-Time Data)

      Move from nightly batches to sub-second events.

      • Batch vs Stream trade-offs
      • Apache Kafka topics, brokers, consumers
      • Producers, schemas, exactly-once semantics
      • Stream processing with Spark Structured Streaming
      • Flink fundamentals
    7. Module 07
      Weeks 13–14

      Cloud Computing & Data Lakes

      Modern lakehouse patterns on real clouds.

      • AWS / GCP / Azure DE stacks
      • Scalable data lake architecture
      • Delta Lake ACID transactions
      • Apache Iceberg & Hudi comparison
      • Storage tiering & governance
    8. Module 08
      Weeks 15–16

      Best Practices & DevOps for Data

      Ship pipelines like a production team.

      • CI/CD for data with GitHub Actions
      • Infrastructure as Code (Terraform)
      • Docker containerization for jobs
      • Data quality with Great Expectations
      • Observability & lineage tools
    Mandatory capstone labs

    Projects you'll actually ship

    Lab 01

    Daily Airflow ETL to Snowflake

    Ingest REST + CSV sources, transform with dbt, land curated tables in Snowflake — all orchestrated by an Airflow DAG with retries and Slack alerts.

    Lab 02

    PySpark Batch Pipeline on Databricks

    Process 100M+ rows of clickstream data with PySpark, apply dimensional modeling, and write partitioned Parquet to a Delta Lake with schema evolution.

    Lab 03

    Kafka → Spark Streaming Lakehouse

    Producers push events to Kafka; a Spark Structured Streaming job writes deduplicated micro-batches into an Iceberg table queryable by dbt.

    1,400+
    DEs placed
    150+
    Hiring partners
    8 yrs
    Training experience
    12
    Max cohort size
    Student stories

    Real placements. Real hikes. Real portfolios.

    "I moved from a backend job to Data Engineering in 4 months. The Airflow + dbt labs matched exactly what my new team runs."
    S
    Sneha Kulkarni
    Data Engineer · fintech
    ₹22 LPA offer
    "PySpark on Databricks was a black box for me before this. Now I own a 200-DAG pipeline for a retail giant."
    R
    Rahul Deshmukh
    Sr. Data Engineer · retail
    70% hike
    "The streaming module was the sharpest one. Kafka + Spark Structured Streaming projects made my resume land interviews at 3 unicorns."
    F
    Fatima Sheikh
    Streaming DE · SaaS unicorn
    Career switch in 5 mo
    "Faculty reviewed lab PRs like a real code review. That level of feedback is rare."
    N
    Nikhil Menon
    Analytics Engineer · Big 4
    Career switch from analyst
    FAQ

    Everything parents & partners ask

    Do I need programming experience?

    Yes — comfort with one language (any) is required. We ramp Python and SQL from intermediate to advanced. If you're brand-new to coding, do our Data Analyst track first.

    Do I get real cloud + Databricks access?

    Yes. Every student gets sandbox credits on AWS/GCP + a Databricks Community edition workspace. Every lab runs on real infra.

    Which cloud does the course focus on?

    AWS is primary; we mirror key labs on GCP so alumni are comfortable on both stacks.

    Is the training online or in-person?

    Hybrid. Weekday online, weekend labs and mock interviews at our Chandra Layout campus in Bengaluru (or fully online for remote learners).

    What roles do alumni get placed into?

    Data Engineer, Analytics Engineer, Platform Engineer (Data) and Streaming Engineer roles — with typical packages of ₹12-28 LPA depending on experience.

    What's the fee and are EMIs available?

    Fee is ₹99,000 all-inclusive. 0% interest EMIs across 6, 9 and 12 months. Scholarships available for women returnees and students from tier-3 cities.

    Next cohort · Aug intake

    Your Data Engineering career starts with a 30-min call.

    Talk to a mentor. Get an honest read on your background, a personalized roadmap, and full fee + EMI details. No pressure.

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