The Forgotten Backbone of AI: Why We Need to Start Valuing Data Engineers Again

Everyone wants to build the future. And right now, that future is spelled AI.

Walk into any classroom or conference, and you’ll hear the same buzz: prompt engineering, generative models, LLMs, autonomous agents. AI is the poster child of modern tech, and rightly so. It’s redefining the way we work, think, and live.

But here's the silent truth: without data engineering, AI would be a beautifully sculpted shell, empty on the inside.

At a recent tech meetup, a speaker posed two simple questions to the audience: “Who wants to be an AI engineer?” Nearly every hand shot up.

Then came the second question: “Who wants to be a data engineer?” One hesitant hand, belonging to a PhD student, went up.

When the speaker asked why he chose data engineering, the student smiled and said: “Because 80% of AI is data engineering.”

The Foundation Everyone’s Ignoring

In the words of Andrew Ng, co-founder of Coursera and one of AI’s leading voices:

“Data is food for AI. Without good data, it’s like trying to train a pilot with broken flight simulators."

We talk a lot about what AI can do, but not enough about what it depends on. Every fancy AI application you see: ChatGPT, self-driving cars, fraud detectors relies on clean, structured, well-pipelined data. That’s not the job of an AI engineer. That’s the data engineer’s battlefield.

And yet, in most educational programs, data engineering barely gets a mention. Students are thrown into deep learning without even understanding where the training data came from or how it was prepared.

No Pipelines, No Progress

In a nutshell:

• No clean data = No useful outcomes.

• No pipelines = No real-time learning.

• No structure = No business insight.

A report by DICE (2023) revealed that while AI job titles are rising, the fastest-growing salaries and demand are in data engineering. Companies are realizing that their AI models can’t function without scalable data infrastructure.

Gartner has even warned in multiple reports that “through 2027, poor data quality will undermine 80% of AI initiatives.”

So while the AI hype is real, the shortage of data engineers is even more real, and more dangerous.

The Risk of an Unbalanced Tech Ecosystem

We’re creating an educational imbalance. Everyone is being trained to "consume" AI, but very few are trained to build its supply chain. In a few years, we might find ourselves with a flood of AI professionals… and a drought of foundational experts.

This isn’t a criticism; it’s a caution.

We need more students learning ETL pipelines, data warehousing, cloud orchestration, database design, and streaming data processing. These are not "backend" roles—they're the heart of modern AI systems.

Let’s Build Smarter

If you're a university student deciding your career path, here’s a gentle reminder: Don’t just chase the spotlight. Learn how the stage is built.

If you’re an educator or curriculum designer: It’s time to balance the curriculum. Build minds that don’t just operate AI, but enable it to exist.

And if you're already working in tech: Give credit where it’s due. Appreciate your data engineers. Include them early in your AI plans. Because they’re not just enablers, they're your lifeline.

Conclusion

GenAI is the face of the future. But data engineers? They’re the bones, muscles, and heartbeat behind it.

As DJ Patil, former U.S. Chief Data Scientist, once said:

“Data science is a team sport—and data engineering is your star player.”

So build with data. And you’ll be able to build anything.