The Quiet Rise of AI’s Real Enablers

The future of AI may depend less on algorithms and more on the people who manage the data. As enterprises scramble to scale their AI efforts, one thing is becoming clear: data engineers are no longer behind the scenes. They’re taking on bigger responsibilities, setting standards, making calls on tooling, and designing the infrastructure that keeps AI from falling apart.

A new report from MIT Technology Review Insights, published in partnership with Snowflake, makes this shift clear. After surveying 400 senior tech leaders, the researchers found that 72 percent now consider data engineers to be critical to the success of their business. That number jumps even higher among the largest companies, where AI maturity is further along. They’re no longer seen as support staff. They’re now central to how companies plan, build, and scale AI.

That rise in importance comes with a lot more pressure. The survey found that most tech leaders say their data engineers are feeling it. More than two thirds (77%) shared that their data engineer’s workload is getting significantly heavier. It is not just more data. It is messier data, moving faster, showing up in formats that older systems were never built to handle.

“Models need so much more data and in multiple formats,” shared George Westerman, Senior Lecturer and Principal Research Scientist, MIT Sloan School of Management. “Where it used to be making sense of structured data, which was relatively straightforward, now it’s: ‘What do we do with all this unstructured data? How do we tag it? How do we organize it? How do we store it?’ That’s a bigger challenge.”

(Source: Redefining data engineering in the age of AI report)

The job of a data engineer has moved from being a builder to more of an architect. Their daily routine has moved far beyond troubleshooting and ETL scripts. These days, more of an engineer’s time is being spent on AI projects that demand new thinking. The report highlights that the use of AI jumped from 19% of the time in 2023, to 37% in 2025, and up to 61% just two years after that.

That’s a big jump. As Ritu Jyoti, group vice president of worldwide AI and automation research at IDC, explains, “Engineers are no longer just managing isolated pipelines. They’re designing platforms that power enterprise-wide AI.” It’s a whole new level of influence, one that stretches from infrastructure to insight.

As engineers get pulled deeper into AI work, their visibility is rising. So is their influence on critical decisions. The report reveals that data engineers are now helping shape tooling choices, infrastructure plans, and even high-level business strategy.

Two-thirds of the leaders say their engineers are involved in selecting vendors and tools. More than half say they help evaluate AI use cases and guide how different business units apply AI models. That represents a shift from execution to influence. These engineers are no longer just implementing someone else’s ideas. They are helping define the roadmap.

It also signals something bigger. AI success is not just about algorithms. It is about coordination. Privacy, cost, speed, accuracy — everything feeds back into how data is handled. That makes the person who understands the flow of data across the stack a lot more important than they used to be.

Chris Child, vice president of product for data engineering at Snowflake, emphasized the urgency: “Realizing the full potential of AI starts with a solid data foundation. That used to be seen as back-end work. Now, it’s viewed as a strategic function. Data engineers are becoming key business partners, trusted with an organization’s most valuable asset — its data. If your C-suite still sees this as a support role, you’re already falling behind. And chances are, you’re training your future competitors.”

(Source: Redefining data engineering in the age of AI report by MIT and Snowflake)

So the role and visibility of data engineers are clearly changing. But are we seeing real gains in productivity? The report suggests yes. More than 70 percent of tech leaders said AI tools are already making their teams more productive. The workload might be heavier, but it’s also more focused. Engineers are spending less time fixing brittle pipelines and more time shaping long-term infrastructure.

The next step may be even more disruptive. Agentic AI has entered the scene. These tools can act semi-independently, suggesting optimizations, managing schema changes, even triggering data tasks without being asked. More than half of the companies surveyed said they’re already deploying or planning to adopt them in the next year.

That means data engineers will face an even more dynamic landscape. And they’re the ones best positioned to handle it. They understand how systems connect, how workflows collide, and what breaks under pressure. If AI is going to scale safely, it will depend on the people who know how to hold the entire ecosystem together. For companies hoping to move fast without falling apart, the data engineer is not just a builder anymore. They’re the glue.

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