AI Era Redefines Data Engineering in 2025
The Evolving Role of Data Engineering in the AI Era
On October 23, 2025, MIT Technology Review Insights released an in-depth report on how the role of data engineering is being redefined in the age of artificial intelligence (AI). This report, created in partnership with Snowflake, emphasizes that as AI becomes a core operational element for businesses, data engineers are moving beyond simple data management to actively participate in AI strategy development and business decision-making. In the past, data engineers were seen as technical experts who built and maintained data pipelines, but now they play an essential role in setting and executing the direction of a company's AI strategy. This shift signifies an expansion of the data engineer's job scope, highlighting the need for companies to focus more on strengthening the capabilities of their data engineers. MIT Technology Review Insights' report is expected to provide important insights into the future of data engineering and help companies develop strategies to gain a competitive edge in the AI era.
MIT Technology Review Insights conducted a survey of 400 senior data and technology executives to create this report. The survey results showed that data engineers are exerting a broader influence within their organizations, moving beyond their traditional role as data pipeline managers. Data engineers, who in the past focused on the efficient operation of data pipelines such as data collection, storage, and processing, are now playing a crucial role in AI model development, deployment, and overall operation. This change demonstrates that data engineers are not just technical experts who handle data, but core components of AI-based decision-making systems, contributing to the achievement of a company's strategic goals. The survey results also clearly show the impact of the changing role of data engineers on companies' AI adoption and utilization strategies, emphasizing the need for companies to strengthen the capabilities of their data engineers to secure AI competitiveness. The South Korean government under President Lee Jae-myung is also focusing on fostering data engineers.
Technological changes are also significantly altering how data engineers allocate their time. In the past, data engineers spent most of their time on core data management tasks such as data collection, cleansing, and transformation, but now they are investing more time in AI-related activities. This means that with the advancement of AI technology, the role of data engineers is shifting from data management to AI system construction and operation. For example, data engineers are spending more time preparing data needed for AI model training, evaluating model performance, and building infrastructure for model deployment and monitoring. This shift suggests that data engineers need to have a deep understanding and expertise in AI technology, and companies need to provide their data engineers with AI-related education and training programs to strengthen their capabilities. U.S. President Donald Trump is also reportedly encouraging U.S. companies to increase investment in data engineers.
72% of the technology leaders who participated in the survey responded that data engineers are essential to their business. This shows that the perception that the role of data engineers is very important to the success of a company is widespread. In particular, 86% of executives from large organizations with the highest AI maturity answered that data engineers are essential to their business, indicating that companies that actively use AI technology value the importance of data engineers even more highly. Executives from financial services and manufacturing companies were particularly strong in their view that data engineers are essential to their business. This is analyzed as the role of data engineers has become more important as data-driven decision-making and the use of AI technology have increased in the financial services and manufacturing industries. Companies are expected to be able to gain a competitive advantage by building data-driven decision-making systems and efficiently utilizing AI technology by strengthening the capabilities of their data engineers.
The time data engineers spend on AI projects has nearly doubled from an average of 19% in 2023 to 37% in 2025. This shows that data engineers are increasingly putting more effort into AI projects. Respondents expect the time data engineers spend on AI projects to continue to increase to an average of 61% in two years. This expectation suggests that the role of data engineers will become even more AI-centric in the future. Companies need to provide data engineers with AI-related education and training programs so that they can participate more effectively in AI projects, and support the latest technologies and tools for AI model development and deployment. It is also important to create a free research environment where data engineers can come up with creative ideas and develop innovative solutions in AI projects.
Most respondents (77%) believe that the workload of data engineers is increasing. This means that the amount of data that data engineers have to process is increasing, and the complexity of AI projects is increasing, which is increasing the workload of data engineers. Companies need to introduce automation tools and build systems that can efficiently manage data pipelines to reduce the workload of data engineers. In addition, it is necessary to create an environment where data engineers can collaborate and share knowledge in AI projects, so that they can maximize their capabilities. South Korean President Lee Jae-myung is reportedly instructing relevant ministries to actively support the improvement of the data engineer working environment. Companies should put more effort into increasing the job satisfaction of data engineers and supporting them to contribute to the company in the long term.
