As organizations embed Artificial Intelligence more deeply across operations, data engineers are emerging as pivotal enablers of these initiatives. A survey of 400 senior data and technology executives by MIT Technology Review Insights finds data engineers now play an influential role that extends beyond traditional pipeline management. With Artificial Intelligence success hinging on reliable, well-managed, high-quality data, respondents view data engineers as integral to realizing business value from advanced technologies.
The report highlights a decisive shift in how data engineers spend their time. On average, their daily involvement in Artificial Intelligence projects has nearly doubled in two years, rising from 19 percent in 2023 to 37 percent in 2025. Executives expect this share to climb further to 61 percent within the next two years, underscoring the growing weight of Artificial Intelligence specific tasks relative to core data management. This expansion is occurring alongside heavier job demands. Most respondents, 77 percent, report that data engineering workloads are increasing.
Growing influence also brings mounting challenges. More advanced Artificial Intelligence models are increasing the complexity of data environments, elevating the importance of managing unstructured data and real-time pipelines. Data teams must sustain reliability and speed while scaling to meet new requirements, which compounds operational strain. These dynamics reflect a broader transition in which data engineering responsibilities are broadening to support more sophisticated, real-time Artificial Intelligence use cases.
The strategic importance of data engineers is widely recognized. Seventy two percent of surveyed technology leaders say data engineers are integral to the business, a view that is even stronger in the largest organizations, where Artificial Intelligence maturity is highest. There, 86 percent share this assessment. Support is especially pronounced in financial services and manufacturing. Taken together, the findings indicate that data engineering is being redefined, with elevated expectations, expanding scope, and increasing workload as organizations push to scale Artificial Intelligence across the enterprise.
