How to Become a Data Engineer in India (2025): Skills, Salary, Tools & Roadmap

The demand for Data Engineers in India is growing rapidly, especially with the rise of AI, data science, fintech, e-commerce, and cloud-based platforms. Every company — from startups to giants like TCS, Infosys, Google, Amazon, Flipkart, and Reliance — needs people who can collect, clean, move, and store data efficiently.

If you’re someone who enjoys solving technical problems, writing code, and working with large datasets, this career could be perfect for you.

Let’s break it down step-by-step — what skills you need, tools to learn, salary, benefits, certifications, career roadmap, and how to land your first job.

Who is a Data Engineer?

A Data Engineer is a tech professional who builds systems that collect, store, and process large volumes of data. They make sure data is reliable, accessible, and ready to be used by analysts, machine learning engineers, or business teams.

Think of a Data Engineer as the “plumber” of the data world — they build pipelines that carry clean, usable data to where it’s needed.

What Does a Data Engineer Do?

Here are some daily tasks of a Data Engineer:

• Build ETL pipelines (Extract, Transform, Load)
• Work with big data tools to process huge datasets
• Manage and design databases and data warehouses
• Integrate APIs, IoT, and real-time data sources
• Clean and preprocess raw data
• Ensure data quality, security, and scalability
• Optimize performance and reduce storage costs
• Collaborate with data scientists and backend engineers

Who Can Become a Data Engineer?

You can become a Data Engineer if you have any of the following:

• BTech / BE (Computer Science / IT / Electronics)
• MCA / BCA
• BSc / MSc (Math, Stats, Data Science, Physics)
• Diploma with strong coding skills
• Even non-tech graduates with the right skills + projects can enter

What matters most is your ability to build and manage data systems — not your degree.

Core Skills You Need to Become a Data Engineer

Let’s break down the technical and soft skills you need:

🔹 1. Programming

• Python – Most commonly used language
• SQL – Essential for querying data
• Optional: Java or Scala

🔹 2. Databases

• Relational: MySQL, PostgreSQL, MS SQL
• NoSQL: MongoDB, Cassandra, Redis

🔹 3. Big Data Tools

• Apache Spark – For fast big data processing
• Apache Kafka – For real-time data pipelines
• Hadoop – For distributed storage and processing

🔹 4. Data Warehousing

• Amazon Redshift
• Google BigQuery
• Snowflake
• Click House

🔹 5. ETL Tools

• Apache Airflow – Open-source scheduler
• dbt (Data Build Tool)
• Talend / Informatica / Matillion

🔹 6. Cloud Platforms

• AWS (S3, Glue, Redshift, Lambda)
• GCP (BigQuery, Dataflow, Cloud Storage)
• Azure (Data Factory, Synapse, Blob Storage)

🔹 7. Bonus Skills

• Docker and Kubernetes
• Git for version control
• Linux commands
• APIs and webhooks

Recommended Certifications

These can help build credibility, especially if you’re a fresher or career switcher:

• 🏅 Google Cloud Professional Data Engineer
• 🏅 AWS Certified Data Analytics – Specialty
• 🏅 Microsoft Certified: Azure Data Engineer Associate
• 🏅 Databricks Data Engineer Associate
• 🏅 Cloudera Certified Data Engineer

You don’t need all of them — even one can make your resume stand out.

Salary of Data Engineers in India (2025)

Benefits of Being a Data Engineer

✔ High demand across industries

✔ Stable and future-proof career

✔ Excellent salary and growth

✔ Remote/freelance opportunities

✔ Chance to work on AI/ML and data science projects

✔ Great path to become a Data Architect, ML Engineer, or even CTO

Step-by-Step Roadmap to Become a Data Engineer in India

Step 1: Master the Basics (Month 1–3)

• Learn Python (functions, OOP, error handling)
• Learn SQL deeply (joins, aggregations, subqueries)
• Understand how databases work (ACID, indexing, schema)

📚 Resources: W3Schools, LeetCode SQL, Python Crash Course

✅ Step 2: Learn Data Engineering Tools (Month 4–6)

• Understand ETL pipelines (manual + automated)
• Start with Pandas, NumPy for data cleaning
• Learn Apache Spark & Kafka
• Install and use PostgreSQL or MongoDB locally

📚 Resources: DataCamp, YouTube (Alex the Analyst, Data with Danny)

✅ Step 3: Get Hands-On with Cloud (Month 6–9)

• Learn basics of AWS/GCP
• Build a project using S3 + Lambda + Redshift
• Explore BigQuery or Snowflake

📚 Resources: FreeCodeCamp, Coursera Specializations, Cloud Skills Boost (GCP)

✅ Step 4: Build Projects & Portfolio (Month 9–12)

Make at least 3-5 good projects and upload to GitHub:

1. Real-time Twitter Sentiment Pipeline using Kafka + Spark
2. Weather Data ETL using Airflow + AWS + Redshift
3. Sales Data Dashboard using SQL + BigQuery
4. IoT Sensor Data Stream (Simulated)
5. Web Scraping Pipeline using Python + MongoDB

✅ Step 5: Apply for Jobs (Month 12+)

• Make a strong resume and LinkedIn profile
• Practice Python & SQL interview questions
• Apply on: LinkedIn, Naukri, Internshala, CutShort, AngelList
• Try internships or contract roles to start
• Don’t wait to be perfect — apply early, learn on the job!

Real-Life Tips for Job Seekers

🔹 Add your GitHub & LinkedIn to your resume

🔹 Share your learning journey weekly on LinkedIn

🔹 Join Discord groups, Telegram, and tech communities

🔹 Follow Indian data engineers & hiring managers

🔹 Do 1 mock interview every week

Final Thoughts & Conclusion

Becoming a Data Engineer is not just about learning tools or passing interviews — it’s about building systems that power decisions, drive innovation, and support billions of data points every day.

Yes, the path can be technical and sometimes overwhelming, especially if you're just starting out. But remember this: you don’t need to know everything on day one. What you do need is a learner’s mindset, curiosity, and the courage to start.

Start small. Learn one tool at a time. Build mini projects. Break things. Fix them. Repeat.

About the Author:

Dikshant Sharma is a Student of Data Science with Bachelor of Computer Applications. Passionate about making complex concepts easy to understand, Dikshant Sharma enjoys helping others navigate the world of data and technology. Connect with me to learn more about data Science and analysis Artificial Intelligence (AI), Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing (NLP)