🔔 Coming Soon — Register Your Interest
Data Engineering
with AI
A comprehensive 1-year live training program covering Apache Spark, Kafka, Airflow, dbt, cloud data platforms, and AI-powered data pipelines. Built for engineers who want to build and manage production-grade data infrastructure.
Data Engineering
with AI
📅 1 Year · 240+ hours live
💻 Live Online — Weekday & Weekend
👥 Max 25 Students per batch
🎓 B.E./B.Tech/BCA/MCA Graduates
🏆 Industry Certificate included
🗓️ Launch date TBA — Register now
What's Coming
Data Engineering is the Backbone of Every AI System
Every AI model runs on data pipelines. Every business decision is powered by data infrastructure. Data Engineers are among the highest-paid tech professionals in India — and demand is growing faster than supply. This program teaches you to build, manage, and scale real data systems.
1 Year
Program Duration
240+ Hours
Live Instruction
Max 25
Students Per Batch
AI-Powered
Pipeline Design
What You'll Build
End-to-End Data Pipeline
Ingest → Transform → Load using Kafka, Spark, Airflow and dbt. Deployed on AWS with monitoring and alerting.
LLM Data Pipeline
Build a pipeline that feeds cleaned, structured data into an LLM — document ingestion, chunking, vector storage, retrieval.
Real-Time Analytics Platform
Streaming data with Kafka + Spark Streaming → Snowflake → live BI dashboard with automated reporting.
Curriculum Preview
What You'll Learn — Module Overview
12 modules covering the complete data engineering journey — from SQL and Python foundations to AI-powered data pipelines in production.
Module 01
Python & SQL Foundations for Data Engineers
- Python for data — Pandas, NumPy, file handling
- Advanced SQL — window functions, CTEs, query optimization
- PostgreSQL deep dive — indexing, partitioning, vacuuming
- Python scripting for ETL automation
- Data quality checks and validation patterns
Module 02
Data Warehousing & Dimensional Modelling
- OLTP vs OLAP — when and why
- Dimensional modelling — star schema, snowflake schema
- Slowly Changing Dimensions (SCD Types 1, 2, 3)
- Snowflake — architecture, virtual warehouses, clustering
- BigQuery fundamentals and cost optimization
Module 03
dbt — Data Build Tool
- dbt architecture — models, sources, tests, docs
- Building transformation layers — staging, intermediate, mart
- dbt tests, macros & packages
- Version control for data transformations with Git
- dbt Cloud — scheduling and deployment
Module 04
Apache Spark — Distributed Data Processing
- Spark architecture — RDDs, DataFrames, SparkSQL
- PySpark for large-scale data transformations
- Spark optimizations — partitioning, caching, broadcast joins
- Running Spark on AWS EMR and Databricks
- Delta Lake — ACID transactions on data lakes
Module 05
Apache Kafka — Real-Time Streaming
- Kafka architecture — brokers, topics, partitions, consumers
- Producers and consumers with Python
- Kafka Connect — ingesting data from databases and APIs
- Kafka Streams & ksqlDB for stream processing
- Managed Kafka on AWS MSK
Module 06
Apache Airflow — Pipeline Orchestration
- Airflow architecture — DAGs, operators, tasks, sensors
- Writing production DAGs in Python
- Airflow connections, variables & XComs
- Error handling, retries & SLA monitoring
- Managed Airflow on AWS MWAA
Module 07
Cloud Data Platforms — AWS & GCP
- AWS data stack — S3, Glue, Redshift, Athena, Lambda
- GCP data stack — GCS, Dataflow, BigQuery, Pub/Sub
- Data lake architecture on S3 with Iceberg
- IAM, VPC and security best practices for data
- Cost management and optimization strategies
Module 08
Project 1 — Batch Data Pipeline
- End-to-end batch pipeline — ingest, transform, load
- Tech stack: Python + Airflow + Spark + dbt + Snowflake
- Data quality testing and monitoring
- Deployed on AWS with CI/CD via GitHub Actions
- Code review and instructor feedback session
Module 09
AI & LLM Data Pipelines
- Data pipelines for LLM training and fine-tuning
- Document ingestion, chunking and embedding pipelines
- Vector databases — Pinecone, pgvector, Chroma
- RAG pipeline engineering — retrieval, ranking, generation
- Monitoring LLM pipelines in production
Module 10
Project 2 — Real-Time + AI Pipeline
- Streaming pipeline with Kafka + Spark Streaming
- Real-time data feeding into an LLM application
- Dashboard with live metrics and anomaly detection
- Deployed on AWS with full observability
- Live demo, peer review and instructor feedback
Module 11
DataOps — Testing, Monitoring & Observability
- Data quality frameworks — Great Expectations, Soda
- Pipeline monitoring — alerting, SLA tracking, lineage
- Data cataloguing with Apache Atlas / OpenMetadata
- Infrastructure as code with Terraform for data platforms
- Incident management and debugging data pipelines
Module 12
Interview Prep, Resume & Mock Interviews
- 3 full mock technical interview rounds with feedback
- Common data engineering interview questions & answers
- ATS-optimized resume & LinkedIn profile workshop
- GitHub portfolio setup — all projects hosted publicly
- Job search strategy for data engineering roles
Why Data Engineering
The Highest-Paid Role in the Data Ecosystem
Data Engineers build the infrastructure that powers every AI model, every business dashboard, and every data-driven decision. Without data engineers, data scientists have nothing to work with.
Highest Salaries in Data Roles
Data Engineers earn ₹8–25 LPA in India, often more than data scientists. Senior Data Engineers at product companies command ₹30–50 LPA.
AI Runs on Data Pipelines
Every LLM, every ML model, every AI product needs clean, structured, reliable data infrastructure. Data Engineers build that foundation.
Fastest Growing Tech Role
Data Engineering job postings have grown 50%+ year-over-year in India. Every company with data — which is every company — needs data engineers.
Remote-First & Global Opportunities
Data Engineering roles are among the most remote-friendly in tech. Companies in the US and EU actively hire Indian data engineers for remote positions.
✅ Who This Program Is For
- Fresh graduates wanting to enter the data engineering field
- Software developers wanting to move into data roles
- SQL / BI developers wanting to upskill to modern data stack
- Anyone targeting roles at product companies or data-first startups
- Engineers interested in building infrastructure for AI systems
✅ Prerequisites
Basic Python and SQL knowledge. Any engineering graduate with programming fundamentals can join. No prior data engineering experience needed.
Coming Soon
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When We Launch
When We Launch
Register your interest and get priority access when Data Engineering with AI opens for enrollment. Early registrants get first pick of batch timings and early bird pricing.
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🏷️ Early bird pricing for registered interest
📅 Priority batch selection
🎁 Free data engineering prep resources while you wait
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Data Engineering with AI — Coming Soon