Cloud Data Engineering Masterclass.
The most complete, job-first data engineering program at LevelUp. You don't learn one tool — you learn to build the pipeline, ship it like software, and make it AI-ready, on Google Cloud and Azure.
Built for the job, not just the certificate.
Most courses teach you to write a pipeline and stop there. Hiring managers in 2026 want engineers who can build, deploy like software, and prepare data for AI.
This program goes deep on Google Cloud, the fastest-growing data cloud, and covers the Azure topics that appear most in job descriptions. Then it adds the two skills that separate juniors from mid-level engineers: shipping with CI/CD, and building GenAI pipelines on company data.
Every module maps to a real job requirement, and every project ends up in your own GitHub repo with an architecture diagram you can talk through in an interview.
GCP first. Azure where it pays.
You get genuinely strong on Google Cloud — BigQuery, Dataflow, Pub/Sub, Composer — and then cover the high-value Azure services so your resume matches both kinds of openings.
The concepts carry across: partitions, streaming, orchestration and lakehouse layers look the same whichever logo is on the console.
Learn the pattern once. Apply it on any cloud.
BigQuery
Partitioning, clustering, streaming inserts and cost control on real data volumes.
Dataflow + Pub/Sub
Apache Beam batch and streaming, with decoupled real-time ingestion.
Cloud Composer
Airflow DAGs that schedule, retry and alert — the orchestration interviewers ask about.
Azure Data Factory
Metadata-driven ingestion into ADLS Gen2, the storage backbone of lakehouses.
Azure Databricks
Spark, Delta Lake and medallion layers: bronze, silver, gold.
Synapse + Snowflake
Enterprise warehousing, with Terraform and GitHub Actions to deploy it all.
Fix the broken pipeline
It's Monday 9 am. The sales dashboard is empty and the CEO is asking why. Four things went wrong overnight. Find them and fix them — like a data engineer would.
Find out why. Click on anything that looks wrong in the pipeline.
1 · INGEST JOBS 1 failed
▶2 · BRONZE → SILVER quality check failed
| order_id | customer | city | amount |
|---|---|---|---|
| 88213 | 4471 | Hyderabad | 1299 |
| 88214 | 4502 | Pune | 549 |
| 88215 | 4390 | Chennai | 2150 |
3 · SILVER · ORDERS_CLEAN schema mismatch
| order_id | status | |
|---|---|---|
| 88213 | 04/10/2026 | paid |
| 88214 | 2026-10-04 | paid |
| 88215 | 4 Oct 2026 | paid |
| 88216 | 10-04-26 | refund |
4 · DASHBOARD slow query
You just did a data engineer's Monday morning.
Retrying a failed job, removing duplicates, standardising dates and tuning a slow query — that's orchestration, data quality, standardisation and performance. Exactly what interviewers ask about, and exactly what you practise in Modules 2 and 4 of this masterclass.
Four projects. Four architectures. Zero copy-paste.
Each one goes from raw source to production: your own GitHub repo, your own architecture diagram, your own demo video to show in interviews.
Real-time food delivery analytics
Orders flow in by the second. Build the streaming backbone that surfaces demand spikes, slow zones and top restaurants as they happen.
Risk and compliance lakehouse
Ingest millions of transactions, refine them through quality gates, and produce the trusted aggregates a risk team can sign off on.
E-commerce warehouse with full CI/CD
Design a proper analytics warehouse, orchestrate it with Airflow, then deploy the whole stack through an automated pipeline. Proof you're more than a notebook engineer.
AI knowledge assistant (RAG pipeline)
Every company wants a chatbot trained on its own documents. Build that retrieval pipeline end to end: ingest, embed, store vectors, serve an LLM that answers from knowledge.
Five modules, in the order real skills compound.
From core foundations to deep GCP, high-value Azure, DataOps and GenAI. Open any module to see exactly what's covered.
Mod 01Foundations and data engineering core
- SQL mastery: window functions, optimisation
- Python for DE: Pandas, PySpark, modular code
- Data modelling: OLTP vs OLAP, star and snowflake, SCDs
- Architecture patterns: batch, streaming, Lambda, Kappa
Mod 02Google Cloud Platform — core focus
- BigQuery: SQL, partitioning, clustering, streaming
- Dataflow: Apache Beam batch and streaming at scale
- Pub/Sub real-time ingestion; Dataproc managed Spark
- Cloud Composer orchestration; IAM and cost governance
Mod 03Azure essentials — the topics that pay
- Azure Data Factory: metadata-driven ingestion
- ADLS Gen2: the storage backbone of lakehouses
- Azure Databricks: Spark, Delta Lake, medallion layers
- Synapse and Snowflake for enterprise warehousing
Mod 04CI/CD and DataOps — ship like a real team
- Git and GitHub: branching, pull requests, code review
- CI/CD pipelines with GitHub Actions and Azure DevOps
- Terraform infrastructure as code across GCP and Azure
- Docker, testing, monitoring and alerting
Mod 05GenAI and LLMs for data engineers
- LLM fundamentals: tokens, embeddings, prompts
- Vector databases: storing and querying embeddings
- RAG pipelines: AI assistants on company data
- GenAI on cloud: Vertex AI and Azure OpenAI; data prep for AI
18 bonus OTT courses, and 15 roles you can apply for.
Enrol and you also unlock the full LevelUp OTT library (an added benefit worth ₹50,000), plus job assistance with resume review and mock interviews.
Foundations
Python, SQL, Pandas, NumPy, Linux, data engineering concepts.
Big data and lakehouse
PySpark, Databricks, Airflow, Microsoft Fabric.
Cloud and DevOps
AWS data engineering, GCP data engineering, Terraform, YAML, GitHub, Docker, ECS, GenAI.
Core engineering roles
Data Engineer, Cloud Data Engineer, GCP Data Engineer, Azure Data Engineer, Big Data Engineer.
Pipeline and platform roles
ETL / ELT Developer, DataOps Engineer, Data Platform Engineer, Streaming Data Engineer, Warehouse / BigQuery Developer.
Growth roles
Analytics Engineer, GenAI Data Engineer, AI/ML Data Engineer, Junior Data Architect, Associate Solutions Engineer.
Typical placement range for LevelUp graduates: ₹12–30 LPA, based on our alumni data. Your outcome depends on your background and effort; we'll tell you honestly in the demo where you stand.
Built for people who want the job, not just the course.
Freshers and students
Start from SQL and Python basics and finish with four cloud projects on GitHub.
Support, testing and legacy developers
PHP, .NET, Java, QA or support. Your coding background means you move fast through the pipeline modules.
Data analysts and BI developers
You know the data. This adds the cloud, orchestration and deployment skills for engineering roles.
Non-IT professionals
Possible, with extra effort on Module 1. Ask us in the demo and we'll be honest about the path.
Before you join: basic computer knowledge. SQL or Python helps but is taught from the start. Live batches on Zoom with recordings on the OTT platform; a laptop and free cloud trial accounts are all you need.
Taught by a practitioner, not a theorist.
Sateesh Pabbathi
"I'm stuck in a low-paying job on PHP, .NET or Java. I want to get into data engineering — but I don't know where to start."
I hear this every week from IT professionals. I get it — I've been there. This masterclass is the program I wish existed then: one path from fundamentals to a deployable, AI-ready pipeline.
I have 22+ years in cloud computing, data engineering and DevOps, from classic enterprise data warehouses to today's cloud platforms like Snowflake, Databricks and BigQuery, on global projects across AWS, Azure and GCP. Today I still work as a Data Architect on enterprise data platforms, so every project, review and interview tip here comes from live work.
I mentor software developers, support engineers and testers working on legacy stacks, and guide them into well-paid cloud data engineering roles at product companies, MNCs and startups.
Before you book the demo
Do I need prior technical knowledge?
No advanced knowledge is needed. Basic programming and computer skills help, and Module 1 takes you step by step from fundamentals to practical skills.
Are classes live or recorded?
Both. Live instructor-led batches on Zoom with mentoring, plus every session recorded on the LevelUp OTT platform with lifetime access.
Will I work on real projects?
Yes. Four end-to-end projects, one per architecture: streaming on GCP, lakehouse on Azure, warehouse with CI/CD, and a RAG assistant. Each lives in your own GitHub repo.
Will I get job assistance?
Yes. Resume guidance, mock interviews, interview preparation and career guidance to help you prepare for relevant openings, plus the LevelUp alumni network.
Do you provide a certificate?
Yes. After completing the program and meeting its requirements, you receive a course completion certificate from LevelUp Cloud Trainings.
What is the free demo class?
A live 90-minute session with Sateesh: real GCP and Azure coding, not a sales pitch. You also get a career roadmap on WhatsApp. Decide after, with zero pressure.
Get the Cloud Data Engineering brochure.
One live session shows you how we teach and where you stand. Seats in each batch are limited so every project gets a proper review.
- Enter your details in the brochure form
- Receive course information from LevelUp
- Review the curriculum, projects and cloud tools