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PG Program in Data Engineering with Agentic & Gen AI

Learn pipelines, ETL, warehousing and orchestration for modern data teams, plus the generative and agentic AI automation now layered on top of them.

Duration8 Months
ModeLive Online
Learners10,000+
Enroll Now - ₹1,55,000Download Brochure
Next BatchJuly 18, 2026
Duration8 Months · Live Online
Industry Projects30+
EligibilityFreshers, Graduates, Experienced

What you’ll gain from this program

This PG Program in Data Engineering with Agentic & Gen AI equips you with pipeline, ETL, warehousing, orchestration and AI automation skills through production-style projects and expert guidance.

SQL & Advanced Data Modeling
Python for Data Engineering
ETL & ELT Pipeline Development
Data Warehousing & Lakehouse Architecture
Airflow Orchestration & dbt
Spark & Distributed Processing
250+ Hrs Data Engineering Content
300+ Hrs Live Pipeline Sessions
Working Data Engineers as Mentors
Data Engineering Job Placement Support
Round-the-Clock Assistance
Data Engineering Curriculum Track
Skill Evaluation Tests
Learning Performance Analytics Dashboard
Professional Community Access
Self-Paced Learning Options
Global Alumni Community
Mock Interview Training
Data Engineering Certificates
Live Gen AI & Agentic Automation Case Studies
SQLPythonETLPipelinesGen AI

PG Program in Data Engineering with Agentic & Gen AI curriculum

A comprehensive curriculum designed by industry experts combining SQL, Python, ETL, Spark, Airflow, dbt, cloud warehousing and agentic AI automation. Master pipeline design, data quality, orchestration and platform operations with hands-on projects.

📚 21 modules · learning roadmap

01Introduction to Data Engineering
  • What data engineers build and own
  • The modern data stack explained
  • OLTP versus OLAP workloads
  • Batch versus streaming paradigms
  • Data engineer career paths
  • Industry trends and outlook
02SQL for Data Engineering
  • Core SQL and set-based thinking
  • Joins, aggregations and subqueries
  • Window functions for analytical work
  • Query plans and execution
  • Indexing and performance tuning
  • SQL engineering exercise set
03Python for Data Engineering
  • Python fundamentals for pipeline code
  • File, API and database I/O
  • pandas for transformation work
  • Writing testable, reusable modules
  • Logging, config and error handling
  • Python pipeline component project
04Data Modeling & Warehouse Design
  • Normalisation versus dimensional modeling
  • Star and snowflake schemas
  • Facts, dimensions and choosing the grain
  • Slowly changing dimensions
  • Data vault concepts
  • Warehouse model design project
05ETL & ELT Fundamentals
  • Extract, transform and load stages
  • ETL versus ELT and when each wins
  • Idempotency and reprocessing safely
  • Incremental loads and change data capture
  • Backfills without breaking downstream
  • First end-to-end ETL pipeline
06Data Ingestion & Source Systems
  • Ingesting from APIs, files and databases
  • Handling schema drift at the source
  • Rate limits, retries and backoff
  • Landing zones and raw layers
  • Ingestion metadata and lineage
  • Multi-source ingestion project
07Data Lakes & Lakehouse Architecture
  • Object storage fundamentals
  • File formats: Parquet, Avro, ORC
  • Partitioning and file size strategy
  • Delta Lake and Iceberg table formats
  • The medallion architecture
  • Lakehouse build lab
08Cloud Data Warehouses
  • Snowflake, BigQuery and Redshift compared
  • Storage and compute separation
  • Loading and unloading data at scale
  • Cost control and query efficiency
  • Access control and data sharing
  • Cloud warehouse project
09Apache Spark & Distributed Processing
  • Why distributed processing exists
  • Spark architecture: driver, executors, tasks
  • RDDs, DataFrames and Spark SQL
  • Shuffles, partitions and skew
  • Performance tuning in Spark
  • Spark batch processing project
10Workflow Orchestration with Airflow
  • DAG concepts and scheduling
  • Operators, sensors and hooks
  • Dependencies, retries and SLAs
  • Parameterised and dynamic DAGs
  • Monitoring and alerting on failures
  • Airflow orchestration project
11Analytics Engineering with dbt
  • dbt models, refs and the DAG
  • Staging, intermediate and mart layers
  • Tests, sources and freshness checks
  • Macros, Jinja and reusable logic
  • Documentation and lineage graphs
  • dbt transformation project
12Streaming Data with Kafka
  • Topics, partitions, producers and consumers
  • Event streaming architecture
  • Exactly-once and delivery guarantees
  • Stream processing fundamentals
  • Batch and streaming together
  • Streaming pipeline project
13Data Quality & Testing
  • Defining data quality dimensions
  • Validation with Great Expectations
  • Anomaly detection on pipeline metrics
  • Contracts between producers and consumers
  • Alerting on quality failures
  • Data quality framework project
14Data Governance, Lineage & Security
  • Cataloguing and metadata management
  • End-to-end lineage tracking
  • PII handling, masking and encryption
  • Access control and row-level security
  • Compliance and retention policy
  • Governance review exercise
15DataOps, CI/CD & Infrastructure
  • Version control for data pipelines
  • CI/CD for data transformations
  • Containerising pipeline workloads
  • Infrastructure as code basics
  • Environments: dev, staging, production
  • DataOps pipeline project
16Pipeline Monitoring & Reliability
  • Pipeline observability and metrics
  • Freshness, volume and schema monitoring
  • On-call and incident handling for data
  • Root-cause analysis of a broken pipeline
  • Cost monitoring and optimisation
  • Reliability instrumentation lab
17Generative AI for Data Engineering
  • Where GenAI genuinely helps a data team
  • AI-assisted SQL and pipeline code
  • Automated documentation and data dictionaries
  • Natural-language querying over the warehouse
  • Verifying AI output before it ships
  • AI-assisted data workflow project
18Vector Databases & Embedding Pipelines
  • Embeddings and vector search explained
  • Chunking and indexing strategies
  • Vector stores: pgvector, Pinecone, Weaviate
  • Building a retrieval pipeline for RAG
  • Keeping embeddings fresh as data changes
  • Embedding pipeline project
19Agentic AI for Data Operations
  • What makes a data workflow agentic
  • Tool use and function calling over data systems
  • Agents for triage, quality checks and remediation
  • Guardrails and human-in-the-loop approval
  • Evaluating and monitoring agent behaviour
  • Agentic data automation project
20Data Engineering Capstone Project
  • End-to-end platform build on real data
  • Ingestion, modeling and transformation layers
  • Orchestrated, tested and monitored pipelines
  • AI-assisted automation layered on top
  • Architecture documentation and write-up
  • Portfolio packaging of the capstone
21Data Career Readiness & Interview Prep
  • Data engineer resume creation and optimization
  • SQL and Python interview preparation
  • Data modeling and pipeline design interviews
  • Behavioral interview training
  • GitHub and LinkedIn portfolio for data engineers
  • 1 year Placement Support for Top Fellows

Internship program

  • Real-world Data Pipeline Projects
  • ETL & Warehouse Build-Outs
  • Orchestration & Scheduling Implementation
  • Data Quality & Monitoring Work
  • Gen AI and Agentic Automation for Data Ops
  • Industry Mentorship by Working Data Engineers

Soft skills program

  • Technical Documentation & Data Communication
  • Stakeholder Requirement Gathering
  • Debugging & Systematic Problem Solving
  • Cross-Functional Collaboration with Analysts & Scientists
  • Ownership & Pipeline Reliability Mindset
  • Professional Networking & Relationship Building

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