Data Engineering Course Online India — with Placement Support
20,000+ data engineering jobs open across India — and most candidates fail the first round because they can't explain how a real pipeline runs in production. You'll build SQL, Python, Spark, Kafka, Databricks, Snowflake, dbt & AWS pipelines live, deploy them on cloud, and defend your choices in mock interviews. Exactly like you will in a real one.
100+
Students Placed
₹4–20 LPA
Graduate Salary Range
4.5
Student Rating
100 Hrs
Live Training (Level 2)
01
The market is screaming for data engineers
This isn't hype. Every company that runs on data (which is every company now) needs someone to build and maintain the pipelines that move it. Here's what the numbers actually say.
6K+
Open jobs in Bangalore alone
That's one city. Add Hyderabad (5,679), Delhi NCR (2,650), Pune, Mumbai — you're looking at 20,000+ openings across India. BFSI accounts for 57% of this demand.
₹18L
Mid-level average. Not senior. Mid-level.
The full range is ₹6–32 LPA. But Databricks and Snowflake specialists command 20–40% more than generalists at the same experience level. Tool choice matters more than years of experience.
57%
Banks and insurance drive the hiring
Barclays, JPMorgan, Goldman Sachs, HSBC — their India GCCs aren't back offices anymore. They build core data infrastructure. And they pay accordingly because they need people who understand compliance.
#1
India leads the world in AI skill demand
NASSCOM's numbers. And every AI system needs a data pipeline feeding it. Engineers who build AI-ready infrastructure — vector stores, feature pipelines, embeddings — earn ₹30–45 LPA at product companies. That's Level 2 territory.
02
The stack that gets you hired
We didn't pick these tools because they look good on a syllabus. We pulled 29,000+ job descriptions from Naukri, LinkedIn, and Glassdoor and taught the tools that appeared most often. If recruiters don't filter for it, we don't waste your time on it.
PostgreSQL
Python
Pandas
Apache Spark
Apache Kafka
Apache Airflow
Databricks
Snowflake
dbt
AWS
Docker
Terraform
Power BI
Git/GitHub
Linux
Gen AI / RAG03
What you'll actually learn — no filler
We scanned 29,000+ live data engineer job postings to build this. Every topic maps to something a recruiter tests for. Two levels — take one or both. Each one stands alone.
level-1-foundation.md
Level 1: Data Engineering Foundation
50 Hours · 2.5 Months · 20 Classes │ ₹35,000
You walk in knowing nothing. Ten weeks later, you have 2 projects live on AWS, a GitHub profile that looks like someone who's been working for a year, and the ability to sit in a SQL interview and solve window function problems from memory while the interviewer watches.
What you'll learn
Data Engineering Fundamentals — OLTP vs OLAP, data lifecycle, warehouses vs lakes
SQL Mastery — JOINs, Window Functions, CTEs, Execution Plans, Indexing (PostgreSQL)
Python for Data Engineering — Pandas, REST APIs, SQLAlchemy, logging, error handling
Linux & Shell Scripting — file ops, SSH, cron jobs, bash automation
Git & GitHub — branching, PRs, portfolio repo from Day 1
ETL Pipeline Development — CSV, JSON, Parquet; end-to-end pipelines in Python
AWS Cloud — IAM, S3, EC2, RDS, Lambda, Glue basics
Data Warehousing — Star & Snowflake schemas, SCDs, Amazon Redshift
Data Visualization — Power BI & Tableau dashboards
2 Capstone Projects deployed on AWS + Mock Interviews
level-2-advanced-genai.md
Level 2: Advanced Data Engineering + Gen AI
100 Hours · 5 Months · 40 Classes │ ₹70,000
This is the level that turns a ₹8 LPA resume into a ₹18 LPA resume. Spark, Kafka, Airflow, Databricks, Snowflake — every company paying ₹15L+ asks for at least three of these. Then we add what nobody else teaches: Terraform, Docker, CI/CD for data pipelines, and India's DPDP Act.
What you'll master
Apache Spark & PySpark — architecture, Spark SQL, joins, optimization (AQE)
Apache Airflow — DAGs, operators, scheduling, SLA monitoring
Apache Kafka — streaming, Schema Registry, CDC with Debezium
Spark Structured Streaming — real-time Kafka → Delta Lake pipelines
Advanced AWS — Glue, EMR, Athena, Kinesis, VPC, KMS security
Databricks & Delta Lake — medallion architecture, Unity Catalog, AutoLoader
Snowflake — Snowpipe, time travel, Snowpark, zero-copy cloning
dbt — models, incremental loads, snapshots, auto-generated docs
Docker & Terraform — containerization + infrastructure as code
CI/CD — GitHub Actions, SQLFluff, dbt test automation
Data Quality — Great Expectations, data lineage, alerting
Governance — India's DPDP Act, GDPR, PII masking, Apache Iceberg
System Design — 1B+ rows/day pipelines, capacity estimation
Gen AI for DE — vector databases, RAG pipelines, feature stores
2 Production Capstone Projects + Senior Interview Prep
04
What graduates actually earn
We pulled these from Glassdoor (19,897 salary submissions) and PayScale — June 2026. Not projections. What people with these skills are actually getting paid right now, broken down by company type.
Level 1 Graduates
Junior Data Engineer · ETL Developer · BI Developer
₹4–8 LPA
Top performers with strong portfolio: up to ₹10 LPA
IT Services (TCS, Infosys, Wipro) ₹3.5–7 LPA
Analytics & Mid-size IT ₹5–8 LPA
Product Startups ₹8–12 LPA
TCS Digital / Infosys SP ₹7–11 LPA
Level 2 Graduates
Data Engineer · Cloud DE · Analytics Engineer
₹8–18 LPA
With 1–3 yrs experience + specialization: up to ₹20+ LPA
IT Services (Upskilled) ₹8–12 LPA
GCCs (Barclays, JPMorgan) ₹12–20 LPA
Product Cos (Flipkart, Swiggy) ₹14–22 LPA
Databricks/Snowflake Specialists ₹18–25 LPA
05
Why learn here — the honest version
01
10 students in a batch. That's it.
Scaler runs 200+. UpGrad runs 50+. Almabetter similar. We cap at 10. If you're confused about why your Spark job failed, you get to ask — not wait three days for a "mentor" to respond on a forum.
02
Your instructor ships code for a living.
Not someone who "worked in data for 10 years" and now trains full-time. A working DevOps + Data engineer who deployed a Terraform module last week and can tell you exactly what went wrong and why.
03
You write code in every single session.
No 45-minute slides followed by 15 minutes of "practice." You open your laptop, connect to a real database, write queries against real data, deploy to real AWS. Every class. From Day 1.
04
We teach what nobody else does.
Terraform, Docker, CI/CD for data pipelines, DPDP Act compliance, data contracts. Ask any GCC recruiter — these are the skills that get resumes past the filter. Every other institute stops at Spark and calls it advanced.
05
Weekend or weekday. You pick.
Live online. If you miss a session, the recording is shared within hours. No penalty, no "you missed it, too bad." Life happens. We plan for it.
06
Placement support that's more than a PDF.
ATS-optimized resume. LinkedIn overhaul. Mock interviews modeled on what Barclays, Flipkart, and Amazon actually ask. Salary negotiation — because the difference between accepting the first offer and negotiating is often ₹3–5 LPA.
06
What it costs
No hidden charges. No "contact us for pricing." Here's every number upfront. EMI available if you need to split payments.
Level 1
Foundation
₹35,000
2.5 Months · 20 Classes · 50 Hours
- SQL, Python, AWS, ETL, Redshift
- 2 capstone projects on AWS
- GitHub portfolio from Day 1
- Resume + mock interviews
- 1-year placement assistance
Level 2
Advanced + Gen AI
₹70,000
5 Months · 40 Classes · 100 Hours
- Spark, Kafka, Airflow, Databricks, Snowflake
- dbt, Docker, Terraform, CI/CD
- Gen AI: RAG, vector DBs, feature stores
- System design interview prep
- Databricks/AWS/Snowflake cert prep
- Salary negotiation masterclass
07
Questions we hear every week
If you're thinking about enrolling, chances are your question is already answered here.
How much does this cost?
Level 1: ₹35,000 for 2.5 months. Level 2: ₹70,000 for 5 months. 0% EMI available — no interest, just split the payments. No registration fees, no material charges, no surprise add-ons.
I've never written code before. Will I survive this?
That's who Level 1 is designed for. We start with "what is a table" and build up from there — SQL, Python, Linux, all from scratch. Our batches regularly include people switching from BPO, manual testing, support roles, and even non-IT backgrounds entirely. By month 2, you're deploying pipelines on AWS. It's intense, but nobody gets left behind in a batch of 10.
What will I actually earn after this?
Level 1 grads land between ₹4–8 LPA at IT services companies. Top performers with strong portfolios and good communication reach ₹10 LPA at product startups. Level 2 grads with 1–3 years prior experience hit ₹8–18 LPA, with GCC and product roles going up to ₹20+ LPA.
Do you help with actually getting a job?
Real support, not a job board link. 1-year placement assistance: ATS-optimized resume, LinkedIn overhaul, mock interviews with recorded feedback, and salary negotiation coaching. The gap between accepting the first offer and pushing back is often ₹3–5 LPA.
What tools do you cover?
Level 1: SQL on PostgreSQL, Python with Pandas, AWS (S3, EC2, RDS, Lambda, Glue), Amazon Redshift, Power BI, Tableau, Git, Linux. Level 2: PySpark, Kafka, Airflow, Databricks with Delta Lake, Snowflake, dbt, Docker, Terraform, GitHub Actions, Great Expectations, and Gen AI tools — Pinecone, pgvector, RAG pipelines.
How do the classes work?
Live on Zoom. Not pre-recorded. 2–3 hours per session, twice a week. Max 10 students — you're not watching a webinar, you're in a working session where the instructor sees your screen and catches mistakes in real time. Weekend and weekday batches available.
Why only 10 students? Is that a marketing gimmick?
No — it's a teaching constraint. When someone's PySpark job throws a cryptic error at 9:30 PM during a live class, the instructor needs to look at their screen, diagnose the issue, and explain what went wrong. You can't do that with 50 students. Scaler and UpGrad scale by recording videos and hiring TAs. We scale by running more batches.
How are you different from Scaler, UpGrad, or Almabetter?
Scaler charges ₹2.8–3.5 lakh and runs 200+ per batch. UpGrad charges ₹1.5 lakh and relies heavily on pre-recorded content. We charge ₹35K for the foundation level and ₹70K for the advanced level, teach live in batches of 10, and the instructor is someone who deployed a Terraform module last week. We also teach Terraform, Docker, CI/CD for data pipelines, and India's DPDP Act — topics nobody else in India covers.
Is data engineering actually a good career bet in 2026?
Here's the data: Bangalore has 11,900+ open DE roles on Glassdoor. Hyderabad has 5,679. Delhi NCR has 2,650+. Mid-level average is ₹18 LPA. BFSI alone accounts for 57% of hiring, and these are ₹14–25 LPA roles at GCCs. India ranks #1 globally in AI skill penetration, and every AI system needs a data pipeline behind it.
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08
20,000+ data engineering jobs in India. Most candidates can't pass the interview.
The jobs aren't the problem. The supply of engineers who can actually design a pipeline, deploy it on cloud, and defend their choices under interview pressure — that's the bottleneck. This course puts you on the right side of it.
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