数据智能工程师的黎明
The Dawn of the Data Intelligence Engineer

原始链接: https://motherduck.com/blog/the-data-intelligence-engineer/

作者提出一个面向 AI 时代的全新角色“Data Intelligence Engineer”。此人通过构建软件代理来自动化数据工程(摄取)和分析工程(转换/建模)。他们将组织知识——如可连接字段和 ARR 等业务定义——编码成代理用于回答问题的共享上下文。他们还维护评估套件,即验证代理是否正确回答已知问题并检测回归的测试。最终目标是自助服务:公司里的任何人都应能使用 Claude 或 ChatGPT 等工具准确提问业务问题、构建应用和可视化,并做出高质量的数据驱动决策。做好后,每个问题都像在咨询公司里最好的分析师。日常工作包括构建框架和数据模型、整理上下文、预防和修复故障、运行评估,以及负责业务所依赖的界面。虽然被称为新头衔,作者指出,随着传统角色逐渐融合,这其实是数据团队中任何人——工程师、分析工程师或分析师——都将越来越多承担的角色。

一篇题为“The Dawn of the Data Intelligence Engineer”的Hacker News帖子链接到一篇MotherDuck文章。它有7 points和两条评论:一位评论者抱怨数据工作分散在许多狭窄岗位中,更偏好T形通才;另一位开玩笑地说这个新头衔只会持续约11个月。
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原文

The jobs to be done for a data person in the age of AI don’t really match any of the typical responsibilities of other data team members. I think it is different enough to merit a new title, which I hereby dub the “Data Intelligence Engineer”

The Data Intelligence Engineer automates the data engineer's work (ingestion) and the analytics engineer's work (transformation and modeling) by creating and conducting software agents. They encode their knowledge of the system into shared context that can be used by other agents to answer questions about the organization. These include things like which fields are joinable but also what someone in the company means when they say “ARR”.

Evals are another big part of their responsibility; these are the system tests that validate that when you ask questions with a known answer, the answer is correct. A good eval suite can give confidence that the types of questions that users ask of the system are going to be answered correctly by the agent. Evals also are a way of knowing when something breaks.

The goal of a Data Intelligence Engineer is to enable self-service. That is, anyone in the organization should be able to open Claude or ChatGPT or another agent and answer questions correctly about the business, use that to build apps and visualizations, and in the end, make high quality decisions with the benefit of data. . When the Data Intelligence Engineer does the job well, everyone who asks a question gets the experience of working with the smartest analyst in the company.

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Day to day, that means they are going to be building frameworks, building the data model, curating the context, preventing things from getting broken, fixing them when they break anyway, running evals, and being accountable for the surfaces the business depends on. We wrote the whole thing up as a job description, because we believe this is the way things are going and it's time to give it a name.

If you're a data engineer, an analytics engineer, or an analyst, you'll recognize parts of this. Those roles were already blending into each other, and morphing into something new. This is a role you play, whatever your title says.

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