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Programs/Azure Data Engineering + GenAI

Data + GenAI Engineer

Azure Data Engineering with one focused, applied GenAI track.

160 hours of Azure Data Engineering plus 40 hours of applied GenAI — from SQL and Python to lakehouse pipelines, analytics, delivery and RAG.

200

Session hours

10

Core modules

2

Capstone weeks

Book a Free Consultation See the Curriculum

Your build path

  1. 01

    Foundations

    SQL + Python + cloud basics

  2. 02

    Pipelines

    ADF + storage + incremental loads

  3. 03

    Lakehouse

    Databricks + PySpark + Delta

  4. 04

    Analytics

    Synapse + Fabric + Power BI

  5. 05

    Delivery

    Git + CI/CD + monitoring + security

  6. 06

    Applied GenAI

    RAG + APIs + evaluation + Vector DB

Your Progression

A clear path from learning to demonstrating skills

Move forward when you can explain and build each stage, not simply when a class ends.

  1. Stage 01 · Foundations01

    SQL + Python + cloud basics

    Evidence: Query a dataset and process a CSV file.

  2. Stage 02 · Pipelines02

    ADF + storage + incremental loads

    Evidence: Rerun a load without creating duplicates.

  3. Stage 03 · Lakehouse03

    Databricks + PySpark + Delta

    Evidence: Turn raw data into validated business tables.

  4. Stage 04 · Analytics04

    Synapse + Fabric + Power BI

    Evidence: Model facts and dimensions; explain a KPI.

  5. Stage 05 · Delivery05

    Git + CI/CD + monitoring + security

    Evidence: Deploy a change and investigate a failed run.

  6. Stage 06 · Applied GenAI06

    RAG + APIs + evaluation + Vector DB

    Evidence: Demo a cited answer and explain its limits.

Curriculum · 200 Hours

10 core modules. One focused GenAI track.

160 hours of Azure Data Engineering + 40 hours of applied GenAI. Capstone implementation is separate.

Data Engineering160h

Stage 01 · Foundations

SQL + Python + cloud basics

You’ll be able to: Query a dataset and process a CSV file.

Stage 01 · Foundations

SQL + Python + cloud basics

You’ll be able to: Query a dataset and process a CSV file.

Stage 02 · Pipelines

ADF + storage + incremental loads

You’ll be able to: Rerun a load without creating duplicates.

Stage 03 · Lakehouse

Databricks + PySpark + Delta

You’ll be able to: Turn raw data into validated business tables.

Includes 4 hours of document and metadata preparation for AI.

Stage 04 · Analytics

Synapse + Fabric + Power BI

You’ll be able to: Model facts and dimensions; explain a KPI.

Stage 04 · Analytics

Synapse + Fabric + Power BI

You’ll be able to: Model facts and dimensions; explain a KPI.

Stage 04 · Analytics

Synapse + Fabric + Power BI

You’ll be able to: Model facts and dimensions; explain a KPI.

Stage 05 · Delivery

Git + CI/CD + monitoring + security

You’ll be able to: Deploy a change and investigate a failed run.

Stage 02 · Pipelines

ADF + storage + incremental loads

You’ll be able to: Rerun a load without creating duplicates.

Stage 02 · Pipelines

ADF + storage + incremental loads

You’ll be able to: Rerun a load without creating duplicates.

Applied GenAI40h

Stage 06 · Applied GenAI

RAG + APIs + evaluation + Vector DB

You’ll be able to: Demo a cited answer and explain its limits.

Total session hours200h

Plus two capstone weeks: practical project implementation is additional to the session hours.

Career Path

Show your skills. Explain your decisions.

Roles to explore

Junior Data EngineerETL / ADF DeveloperData Operations Engineer

Build toward GenAI application work as you gain delivery experience and deepen your portfolio.

Start your data engineering journey.

Ask about prerequisites, batch schedule, delivery format, fees and cloud lab access.

Book a Free Consultation Call +91 99646 13666

Also see: Generative AI & Agentic AI