Think of data engineering as plumbing for the digital world.
All those smart dashboards, AI models and analytics reports don’t work unless someone builds the pipelines that bring clean, usable data to them.
That someone is a data engineer.
Why data engineers are suddenly in the spotlight
AI is everywhere today – from product recommendations and fraud detection to diagnostics in health care. But AI is only as good as the data it gets. And that’s the real bottleneck.
Enter the data engineer: the person who makes sure data is collected, cleaned, organized, and delivered to the right place, in the right format, at the right time.
This isn’t just a technical support role. It’s mission-critical.
Why is everyone hiring data engineers now?
Because businesses are drowning in data.
From mobile apps to customer support chats, websites to warehouses, data is being generated everywhere. But that data is:
- Messy
- Scattered
- Hard to use
That’s where a good data engineer comes in. Companies need professionals who can organize this chaos and make data usable, fast and secure.
And the demand is exploding:
- NASSCOM’s 2023–24 Digital Talent Report highlighted a shortage of over 1.4 million professionals in data and AI roles, and Data Engineering forms a substantial share of that gap.
- According to the Global Institute for AI, job postings for AI roles have surged by 119% over the past two years. Similarly, data engineering positions have seen a 98% increase.
- Companies like Tech Mahindra, Accenture and Infosys are actively hiring for data modernization and cloud transformation projects, and data engineers are central to that strategy.
If you’re working in IT, analytics, or reporting, you’re likely seeing this shift already.
Who makes a good data engineer?
You don’t need to be a “10x coder” to get started.
Many successful data engineers come from:
- Excel-heavy or MIS roles.
- Business analyst or reporting jobs.
- Cloud support or QA backgrounds.
If you’re good at structuring information, have a basic grasp of SQL or Python, and understand business data, you’re already halfway there.
What skills do you actually need?
Here’s a simplified roadmap for early- and mid-career professionals:
Must-haves:
- SQL (your bread and butter).
- Data pipelines (ETL/ELT concepts).
- Cloud basics (Azure, AWS, or GCP).
- Data modeling and warehousing (star schema, Snowflake, etc.).
Good-to-have:
- Python for automation.
- Airflow or similar orchestration tools.
- APIs & data integration methods.
- Hands-on with platforms like SASÒ ViyaÒ, Spark, or Databricks
Where to learn it?
There are plenty of ways to learn – bootcamps, online videos or formal programs.
Whether you’re searching for a data engineering course in India, a beginner-friendly data management certification, or a structured training program for working professionals, it’s crucial to choose one that focuses on both core data skills and hands-on cloud projects.
If you’re looking for something structured and industry-aligned, the SAS Data Engineer Program is worth checking out. It’s designed for professionals who don’t want to waste time guessing what to learn next.
Final word: This is a career, not just a skill
Data engineering isn’t a shortcut to becoming a data scientist or AI specialist. It’s a core career track in itself, with huge long-term potential.
You’ll be the one enabling real-time analytics, building data platforms and powering AI systems. In an increasingly automated world, this is one role that won’t be.
Your questions answered
Is data engineering suitable for non-programmers?
Yes. Many data engineers come from Excel, MIS, or analytics backgrounds. You don’t need to be a hardcore coder to get started – SQL, cloud basics, and curiosity are enough.
How long does it take to become job-ready?
With consistent effort, 4–6 months is realistic, especially with the right guidance and hands-on projects.
Is this role future-proof?
Absolutely. With AI going mainstream, structured and reliable data is more important than ever. Data engineers are critical in making AI possible.
