A data engineer builds the pipelines that collect, clean and store data so analysts and machine-learning teams can use it: batch and streaming jobs, data lakes and warehouses, and orchestration.
Open any step to see its lessons, tools and lab, and mark it as learning or done. Your progress is saved in this browser only.
Write clean, working Python programs
Build a command-line tool that cleans and summarises CSV data
Data Engineering: Spark, Databricks & Microsoft Fabric · Generative AI & RAG Engineering · AI Agents with LLMs
Query and analyse data confidently with SQL
Answer 20 business questions from a sales database
Data Engineering: Spark, Databricks & Microsoft Fabric · Data Analytics with AI · Forward Deployed Engineer Program
Collaborate on code with Git like a professional team
Run a team pull-request workflow end to end
MLOps Engineering: Notebook to Production · DevSecOps: Secure CI/CD & Supply Chain · Getting Started with Cloud Automation
Clean, analyse and visualise data with Python
Exploratory analysis on a real dataset
Data Analytics with AI · Data Science Fundamentals · Data Analytics
Build reliable data pipelines at scale
Batch and streaming pipeline into a lakehouse
Choose and model NoSQL databases
Product catalogue on MongoDB with a Redis cache
Data Engineering: Spark, Databricks & Microsoft Fabric · Database Administration: SQL & NoSQL
Choose and use the right Azure data services
Build an Azure SQL database with a live dashboard
Data Engineering: Spark, Databricks & Microsoft Fabric · Azure Solutions Architect Expert (AZ-305) · Microsoft Azure Data Fundamentals (DP-900)
Build interactive dashboards decision-makers use
Executive dashboard for a retail business
Data Analytics with AI · Data Analytics · Power BI & Tableau
Present your skills confidently to employers
Mock technical + HR interview with feedback
Data Engineering: Spark, Databricks & Microsoft Fabric · Data Analytics with AI · Generative AI & RAG Engineering
A practical, step-by-step plan. Skip any step you already know.
3 steps · about 28 hours
3 steps · about 32 hours
2 steps · about 22 hours
1 steps · about 6 hours
Use the hands-on labs in each step, publish your work on GitHub and write a short README for each project. Projects are what interviewers ask about first.
Practise the questions asked for this role, do mock interviews and polish your resume and LinkedIn profile. Practise Data Engineer interview questions →
Start with roles that match your current level, keep learning on the job, and grow towards the senior skills in this path.
Indicative yearly pay, in lakh rupees (LPA). Pay varies with city, company, skills and certifications, so use these as a guide, not a promise.
Rounded ranges from public salary surveys (AmbitionBox, Glassdoor and PayScale India), checked November 2026. Bangalore, Hyderabad and Pune often pay above these ranges, and product companies and global capability centres pay more than service companies.
Data engineers build and run the pipelines and storage. Data analysts use that data to answer business questions with reports and dashboards.
Our advisors will match the path to the right SOTT course for your background.
