Verified IT Work Experience Program

Data Engineering & Big Data Systems Internship

Build scalable data pipelines, ETL/ELT workflows, Apache Spark batch and streaming jobs, and Snowflake data warehouses.

3 - 6 Months
Online / On-site
Experience Letter + LOR
1-on-1 Lead Mentorship
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Role Overview

Step into the shoes of a Big Data Engineer. Work with production distributed architectures including Apache Spark, Kafka streaming clusters, Delta Lake ACID lakehouses, Snowflake cloud warehouses, and orchestrate automated enterprise pipelines using Apache Airflow.

Tools & Stack You Will Use

Python for Big Data Apache Spark & PySpark Apache Airflow Orchestration Snowflake Cloud Data Warehouse Apache Kafka Event Streaming Delta Lake & Parquet Formats Data Quality with Great Expectations Dockerized Big Data Clusters

What You Will Receive

  • Verified Experience Certificate: Company-issued credential recognizing duration.
  • Letter of Recommendation (LOR): Signed recommendation from tech lead for top performers.
  • Production GitHub Repos: Live pull requests, commits, and verified public repositories.
  • Interview & Placement Support: Direct client interview opportunities & referrals.

Eligibility & Candidates

  • Final year & pre-final engineering/IT students
  • Fresh graduates looking for company experience
  • Career switchers seeking real-world portfolios

Commercial Projects You Will Build

Real-world product blueprints and enterprise deliverables

1 Modern Data Engineering & Distributed Systems Architecture

Live Milestone
  • Data engineering landscape: OLTP vs OLAP, Data Warehouses vs Data Lakes vs Lakehouses
  • Columnar file formats: Parquet, ORC & Avro compression and schema evolution
  • High-performance Python for data engineers: Memory-efficient generators, multiprocessing & Polars
  • SQL for big data: Advanced window functions, CTEs, lateral joins & partitioning

2 Distributed Processing with Apache Spark & PySpark

Live Milestone
  • Spark internals: Driver, executors, DAG engine, Catalyst Optimizer & Tungsten execution
  • PySpark DataFrame API: Transformations, actions, caching & broadcast joins
  • Mitigating data skew: Repartitioning, coalesce, salting & spill-to-disk minimization
  • Building robust batch data processing pipelines across gigabyte/terabyte datasets

3 Lakehouse Architecture & Delta Lake ACID Storage

Live Milestone
  • Implementing the Medallion Architecture: Bronze (Raw), Silver (Cleaned), Gold (Aggregated)
  • Delta Lake ACID transactions, time travel (data versioning) & schema enforcement
  • Optimizing Lakehouse performance: Z-Ordering, file compaction & vacuuming old versions
  • Enforcing automated data quality gates using Great Expectations and Soda Core

4 Real-Time Streaming with Apache Kafka

Live Milestone
  • Kafka architecture: Brokers, topics, partitions, consumer groups & offset management
  • Producing and consuming high-throughput event streams with Python / Kafka clients
  • Spark Structured Streaming: Processing real-time streaming data with tumbling & sliding windows
  • Handling out-of-order events using watermarks and stateful stream processing

5 Cloud Data Warehousing with Snowflake & dbt

Live Milestone
  • Snowflake architecture: Centralized storage, multi-cluster virtual warehouses & cloud services
  • Data ingestion: Snowpipe automated streaming ingestion from AWS S3 buckets
  • Transforming raw warehouse data using dbt (data build tool): Models, tests & documentation
  • Zero-copy cloning, time travel & data masking policies for strict compliance

6 Pipeline Orchestration with Apache Airflow & Production Capstone

Live Milestone
  • Architecting Directed Acyclic Graphs (DAGs) in Apache Airflow with custom operators
  • Task dependencies, dynamic DAG generation, SLAs & automated retry mechanisms
  • Containerized Airflow deployment using Docker and Kubernetes Executor
  • Commercial Capstone Defense: End-to-End Enterprise E-Commerce Data Lakehouse Pipeline

Agile Work Culture & Daily Routine

How you collaborate inside your developer pod each week

1

Daily Standups & Sprints

Participate in 15-minute morning standups, track sprint backlog items in Jira, and define daily feature goals.

2

Feature Engineering

Write clean, modular code following design patterns, database migrations, and API contract specifications.

3

PR Reviews & Quality Gates

Submit GitHub pull requests, receive 1-on-1 feedback from senior tech leads, and pass automated CI test pipelines.

4

Cloud Staging & Demo

Deploy live containerized features to cloud staging environments and demonstrate working software during sprint reviews.

Local Internship Campuses & Dev Centers

In-person & hybrid developer seats in Coimbatore & Chennai

Coimbatore • Internship

Bootcamp: Data Engineering & Big Data Technologies
Chennai • Internship

Bootcamp: Data Engineering & Big Data Technologies
Limited Seats Per Batch

Start Your Professional Journey Today

Join our Data Engineering & Big Data Systems Internship and gain verified company work experience, senior mentorship, and a production-grade portfolio.

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