Data Engineers: The Unsung Architects Powering AI Transformations
Data engineers moving into the spotlight
As organizations embed AI into more of their operations, senior leaders are recognizing that data engineers are central to turning those initiatives into reality. AI depends on large volumes of reliable, well-managed, high-quality data — and that is precisely the domain where data engineers operate. The MIT Technology Review Insights report finds that data engineers are now viewed as enablers of AI and integral to business success.
Survey findings: expanded influence and shifting priorities
The conclusions are based on a survey of 400 senior data and technology executives conducted by MIT Technology Review Insights. The survey shows data engineers’ influence has stretched far beyond pipeline maintenance. Their daily activities are shifting from traditional data management tasks toward AI-specific work: the average share of time spent on AI projects rose from 19% in 2023 to 37% in 2025, and respondents expect it to reach an average of 61% within two years. That shift aligns with organizations placing more strategic weight on AI outcomes.
Growing complexity and heavier workloads
Alongside rising influence, data engineers face mounting challenges. Advanced AI models increase the importance of handling unstructured data and maintaining real-time pipelines, which adds architectural and operational complexity. Workloads are expanding too: 77% of respondents reported that data engineering workloads are getting heavier. Teams are being asked to deliver more, faster, and with higher reliability than before.
Variations by organization size and industry
The survey highlights that perceptions of data engineers’ business importance vary by organization size and sector. Overall, 72% of technology leaders view data engineers as integral to the business, but that figure rises to 86% among the largest organizations — those with the highest AI maturity. The sentiment is especially strong in financial services and manufacturing, where data quality and pipeline robustness directly affect core operations.
What this means for companies
As data engineers take on more AI-related responsibilities, companies should reassess resourcing, tooling, and processes. Investments in infrastructure that supports unstructured data processing, real-time streaming, observability, and automation can help teams manage complexity and scale workloads. Organizations that recognize and support the evolving role of data engineers are better positioned to realize the promised value of AI.
About the report
This analysis summarizes findings from a survey by MIT Technology Review Insights of 400 senior data and technology executives. The original content was produced by Insights, the custom content arm of MIT Technology Review, and was researched, designed, and written by human contributors. AI tools, if used, were limited to secondary production processes and reviewed by humans.