Become a job-ready Data Engineer with AI
Master Python, SQL, Apache Spark, Airflow and cloud data warehouses — then go further and build the data pipelines that power Gen AI: RAG pipelines, vector stores and agentic data workflows, with AI-assisted engineering throughout.
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A free 15-minute call to map your path to a job-ready Data Engineer with AI — honest guidance, zero pressure.
- Personalised learning roadmap
- Full syllabus, batch dates & fees
- Scholarships & no-cost EMI options
- Placement & interview support
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The backbone of every
AI system is a data pipeline
Every dashboard, machine-learning model and Gen AI application runs on data that someone had to move, clean and model. That someone is the data engineer. Now that companies are racing to ship RAG assistants and AI agents, the engineers who can build the pipelines that feed them — from raw sources to vector stores — are among the hardest roles to fill.
Data engineering is consistently among the fastest-growing and best-paid technology roles.
Every Gen AI feature needs a data pipeline behind it — engineers who can build them are scarce.
Portfolio pipelines — including AI data pipelines — you can walk an interviewer through end to end.
Ingestion, processing, orchestration, warehousing and AI — the complete modern data skill set.
Built for four kinds
of people
Whatever you’re starting from, the course begins at fundamentals and takes you to job-ready — with the AI data-pipeline skills employers now expect.
Students & freshers
Final-year students and recent graduates who want a real, in-demand skill and a project portfolio before applying.
Non-IT career switchers
Coming from a non-technical background. We start at zero — no coding or database experience assumed.
Analysts & SQL users
Data analysts, BI and support professionals moving from querying data into building the pipelines behind it. Weekend batches available.
Developers & self-taught coders
You know some Python or SQL but need structure, real data projects and someone to review your pipelines properly.
Four phases, twelve modules,
one AI-ready data engineer
Every module ends in hands-on work. Nothing here is theory-only.
Python & SQL foundations
Start from programming logic and finish able to write clean Python and advanced SQL, model data properly and reason about how it moves — using AI coding assistants the way modern teams do.
Python for data engineering
Weeks 1–3Programming logic·Data types & control flow·Functions & modules·Files & APIs·Pandas for data wrangling·Working with JSON, CSV & Parquet·Virtual environments·AI pair-programming (GitHub Copilot)
Advanced SQL & data modeling
Weeks 3–5RDBMS concepts·Joins & subqueries·Window functions·CTEs & aggregations·Indexes & query tuning·Normalisation·Dimensional modeling & star schemas·Slowly changing dimensions
ETL/ELT concepts & data warehousing basics
Weeks 5–7Batch vs streaming·ETL vs ELT·Data sources & extraction·Transformation patterns·Loading strategies·Data quality & validation·OLTP vs OLAP·Warehouse, lake & lakehouse concepts
Big data & pipelines
Process data at scale and move it reliably — distributed processing with Spark, scheduled pipelines with Airflow, and real-time streams with Kafka.
Apache Spark & big data processing
Weeks 7–9Distributed computing·Spark architecture·RDDs & DataFrames·PySpark·Spark SQL·Partitioning & shuffles·Performance tuning·Handling large datasets
Workflow orchestration with Apache Airflow
Weeks 9–11DAGs & tasks·Operators & sensors·Scheduling & backfills·Dependencies & retries·XComs & connections·Idempotent pipelines·Monitoring & alerting·Pipeline best practices
Streaming data with Apache Kafka
Weeks 11–13Event streaming concepts·Topics, partitions & offsets·Producers & consumers·Consumer groups·Kafka Connect·Stream processing basics·Exactly-once delivery·Real-time ingestion patterns
Cloud warehousing, dbt & AI foundations
Land data in the cloud, transform it the analytics-engineering way, and learn how LLMs, embeddings and vector search work — ready to build AI data pipelines.
Cloud data warehousing & data lakes
Weeks 13–15Snowflake & BigQuery·Cloud storage & data lakes·Columnar formats & Parquet·Partitioning & clustering·Loading & unloading·Cost & performance·Lakehouse architecture·Access control
dbt & data transformation
Weeks 15–17Analytics engineering·dbt models & refs·Staging & marts·Tests & documentation·Snapshots & seeds·Macros & Jinja·Incremental models·Version control & CI
AI foundations for data engineers
Weeks 17–19How LLMs work·Tokens & embeddings·Prompt engineering basics·Calling AI APIs from Python·Vector search concepts·Chunking & text pipelines·Responsible & safe AI·Cost & latency basics
AI data pipelines & career
Build the data pipelines that power Gen AI and agents — then ship an AI-ready data platform and get interview-ready.
Gen AI data pipelines & vector databases
Weeks 19–21Building RAG data pipelines·Document ingestion & chunking·Embedding generation at scale·Vector databases (pgvector, Pinecone, Chroma)·Indexing & retrieval·Refresh & freshness·LLM-assisted data engineering·Evaluation basics
Agentic AI data workflows & MCP
Weeks 21–23What agentic workflows are·Tools & function calling·Agentic data pipelines·Model Context Protocol (MCP)·Connecting agents to data sources·Orchestrating AI + data tasks·Guardrails & observability·Cost control
Deployment, AI-ready data platform capstone & interview prep
Weeks 23–26Git & GitHub·Docker·CI/CD intro·Cloud deployment (AWS / GCP)·DataOps & monitoring·AI-ready data platform capstone·Portfolio & resume·Mock interviews
Want the full module-by-module syllabus?
We’ll send the complete curriculum PDF along with batch dates and fee details.
You graduate with a
portfolio, not just notes
Four complete data pipelines — including AI data pipelines — built the way real teams work: version controlled, orchestrated, tested and deployed.
End-to-end ETL/ELT pipeline
Ingest from APIs and files, transform and model the data, and load it into a cloud warehouse — orchestrated on a schedule with Airflow and validated with data-quality checks.
Spark big-data processing job
Process a large dataset with PySpark — partitioning, joins and aggregations at scale — writing optimised Parquet output to a data lake.
RAG data pipeline & vector database
Build a pipeline that ingests documents, chunks and embeds them at scale, and loads them into a vector database to power retrieval-augmented generation.
Capstone — AI-ready data platform
Design and ship a data platform that serves both analytics and AI — batch and streaming ingestion, warehouse models and a vector store — with mentor guidance and review at every stage.
The complete
working stack
Languages & querying
- Python
- SQL
- Pandas
- PySpark
- Bash
- Git
Processing & big data
- Apache Spark
- Spark SQL
- Distributed computing
- Batch processing
- Parquet
- Partitioning
Orchestration & streaming
- Apache Airflow
- DAGs
- Apache Kafka
- Kafka Connect
- Event streaming
- Scheduling
Warehousing & transformation
- Snowflake
- BigQuery
- Data lakes
- dbt
- Dimensional modeling
- Data quality
AI & Generative AI
- RAG pipelines
- Vector databases
- LLM APIs
- Embeddings
- Agentic data workflows
- MCP
- Prompt engineering
- GitHub Copilot
Cloud & DevOps
- AWS
- GCP
- Docker
- CI/CD
- GitHub
- DataOps
- Monitoring
Roles you can apply for
when you finish
The same skill set opens several doors — from core data-engineering roles to the fast-growing AI-data titles our learners now target.
Data Engineer
Build and own the pipelines that move, clean and model data across the organisation.
Analytics Engineer
Turn raw warehouse data into trusted, tested models with dbt for analysts and BI teams.
Big Data Engineer
Process data at scale with Spark and distributed systems for high-volume workloads.
ML / AI Data Engineer
Build the RAG, embedding and vector-store pipelines that feed machine-learning and Gen AI systems.
ETL Developer
Design and maintain reliable extraction, transformation and loading workflows.
Data Platform Engineer
Build and operate the warehouses, lakes and orchestration that other teams depend on.
Resume & portfolio
We rewrite your resume around your pipelines and set up a GitHub that reads well to a reviewer.
Interview drills
SQL, Python, Spark, data-modeling and AI-pipeline question banks, practised until the answers are automatic.
Mock interviews
Technical and HR rounds with working data engineers, followed by honest feedback.
Hiring introductions
Your profile shared with our hiring-partner network as suitable roles open up.
Pick a batch
that fits your week
EDIT: replace the dates and timings below with your actual batch schedule.
Weekday morning batch
Weekday evening batch
Weekend batch — for working professionals
What every enrolment includes
- Live instructor-led sessions with recordings for revision
- Small batches with dedicated doubt-clearing time
- Four portfolio pipelines — including AI data pipelines — with mentor code review
- Resume building and mock interview rounds
- Placement assistance that continues after your last class
- Course completion certificate
Everything you’re
probably wondering
EDIT the answers to match DSU’s actual policies before publishing.
No. Module 1 starts with Python and programming logic from first principles, and Module 2 builds SQL from the basics. Many of our data-engineering learners come from non-IT and analyst backgrounds and have never built a pipeline before.
The program runs 6–7 months — the extra time over a plain data-engineering course covers the AI-foundations, Gen AI data-pipeline and agentic-AI modules. Weekday batches are two hours a day; weekend batches are three hours per day across Saturday and Sunday. Plan for a similar amount of practice time outside class — that’s where the learning consolidates.
EDIT: state your fee here, along with any instalment or EMI options. Being upfront about pricing on this page will improve enquiry quality — people who can’t find a price often assume the worst and leave.
We provide placement assistance — resume preparation, mock interviews and introductions to our hiring-partner network — and that support continues after your final class. We don’t promise a guaranteed job, because no honest training provider can. What we can promise is that you’ll finish interview-ready with real pipelines to show. EDIT this to match your policy exactly.
Yes. Book a free demo session and a one-to-one counselling call. You’ll meet the trainer, see how a session runs and get the full syllabus before committing to anything.
Every class is recorded and available to you, so you can catch up on your own schedule. You can also raise anything unclear in the dedicated doubt-clearing sessions.
No. The AI modules start from the basics — how LLMs work, embeddings, and how to build the data pipelines that feed them. There’s no heavy maths: the focus is on engineering the pipelines behind Gen AI — RAG ingestion, vector databases and agentic data workflows — not on AI research. You get both the core data-engineering skills employers hire for and the AI edge that sets you apart.
Become an AI-ready data engineer
Book a free counselling session. We’ll walk you through the syllabus, batch options and fees — and tell you honestly whether this course is right for you.