内容来源：https://www.technologyreview.com/2025/10/23/1125651/redefining-data-engineering-in-the-age-of-ai/

内容总结：

【新闻总结】随着人工智能技术日益成为企业核心驱动力，数据工程师的角色正从幕后走向战略前沿。根据《麻省理工科技评论》 Insights 联合Snowflake发布的调研报告，通过对400名资深数据与技术高管的访问发现，数据工程师已突破传统数据管道管理的职责范畴，成为企业AI战略落地的关键推动者。

报告指出，72%的技术领导者认为数据工程师对企业发展至关重要，在AI应用成熟度最高的大型企业中这一比例高达86%。随着AI项目占比激增，数据工程师投入AI相关工作的时间从2023年的19%跃升至2025年的37%，预计两年内将达61%。与此同时，77%的受访者表示其工作负荷持续加重。

面对AI技术革新，数据工程师正面临双重挑战：一方面需要应对非结构化数据和实时数据流带来的技术复杂度提升，另一方面需在持续扩张的工作范围内保持高效输出。金融与制造业企业高管尤其强调，可靠的高质量数据管理已成为AI价值兑现的前提条件。

（本文基于第三方机构调研报告编译整理，内容不代表本平台观点）

中文翻译：

赞助内容
用人工智能时代重塑数据工程
随着人工智能逐渐成为企业的核心驱动力，数据工程师正从幕后走向台前，助力制定AI战略并影响商业决策。

本文与Snowflake联合呈现
当企业将人工智能深度融入业务运营时，高管们逐渐意识到数据工程师是实现AI落地的关键角色。毕竟，唯有依托海量可靠、管理完善的高质量数据，人工智能才能真正发挥价值。本报告发现，数据工程师正作为AI的推动者，在企业中扮演着至关重要的角色，并成为业务整体成功不可或缺的一部分。

根据《麻省理工科技评论》洞察团队对400名数据与技术高管的调研，数据工程师的影响力已远超其作为数据管道管理者的传统职责范围。技术变革同时改变了他们的工作模式：其时间分配正从核心数据管理任务向AI专项工作倾斜。

随着影响力的提升，数据工程师面临的挑战也日益加剧。首要挑战是处理更复杂的场景——先进AI模型凸显了管理非结构化数据与实时管道的重要性。另一重挑战是应对持续增长的工作负荷：数据工程师的任务量已达历史高峰，且这一趋势仍将持续。

报告核心发现如下：

• 数据工程师与企业命运紧密相连：72%的技术领导者认同这一观点，在AI成熟度最高的大型企业中，这一比例高达86%。金融服务与制造业的高管对此尤为认同。
• AI正在重塑数据工程师的工作内容：过去两年间，数据工程师投入AI项目的日均时长占比从2023年的19%跃升至2025年的37%。受访者预计该数字将在两年后攀升至61%。这也导致工作负荷加剧，77%的受访者认为其负担正持续加重。

本文由《麻省理工科技评论》定制内容团队Insights撰写，未经编辑部采编。
所有内容均经人类作者、编辑、分析师及插画师完成，包括问卷撰写与数据收集。若使用AI工具，仅限辅助生产环节且经过严格人工审核。

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英文来源：

Sponsored
Redefining data engineering in the age of AI
As AI becomes central to the enterprise, data engineers are stepping out from behind the scenes to help shape AI strategy and influence business decisions.
In partnership withSnowflake
As organizations weave AI into more of their operations, senior executives are realizing data engineers hold a central role in bringing these initiatives to life. After all, AI only delivers when you have large amounts of reliable and well-managed, high-quality data. Indeed, this report finds that data engineers play a pivotal role in their organizations as enablers of AI. And in so doing, they are integral to the overall success of the business.
According to the results of a survey of 400 senior data and technology executives, conducted by MIT Technology Review Insights, data engineers have become influential in areas that extend well beyond their traditional remit as pipeline managers. The technology is also changing how data engineers work, with the balance of their time shifting from core data management tasks toward AI-specific activities.
As their influence grows, so do the challenges data engineers face. A major one is dealing with greater complexity, as more advanced AI models elevate the importance of managing unstructured data and real-time pipelines. Another challenge is managing expanding workloads; data engineers are being asked to do more today than ever before, and that’s not likely to change.
Key findings from the report include the following:

• Data engineers are integral to the business. This is the view of 72% of the surveyed technology leaders—and 86% of those in the survey’s biggest organizations, where AI maturity is greatest. It is a view held especially strongly among executives in financial services and manufacturing companies.
• AI is changing everything data engineers do. The share of time data engineers spend each day on AI projects has nearly doubled in the past two years, from an average of 19% in 2023 to 37% in 2025, according to our survey. Respondents expect this figure to continue rising to an average of 61% in two years’ time. This is also contributing to bigger data engineer workloads; most respondents (77%) see these growing increasingly heavy.
  This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
  This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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