Thought experiment below -- Setup: In the data warehouse world, there is a very interesting trend happening with the rise of the "Analytics Engineer". Histori

Thought experiment below -- Setup: In the data warehouse world, there is a very interesting trend happening with the rise of the "Analytics Engineer". Historically, all data transformations had to happen with "handwritten python/hadoop/spark code" by a software engineer. This process is expensive and slow, because they are often translating SQL code written by a Data Analyst into python/spark code. But with dbt [1], we see Data Analysts becoming Analytics Engineers. They know a lot about SQL, a lot about the dbt framework, a bit about jinja macros, a bit about yaml, and a bit about the command line - now they can create some really powerful data transformations which is the underpinning of the "Modern Data Stack". Thought experiment: Right now, we have a problem where any change to a figma design requires a software engineer to update the react application. What about if tools like makers enabled a "dbt-like" workflow, where a "Component Engineer" can turn a figma component into a "simple react component". A component engineer's work flow would be based on makers, storybook, and a cloud IDE. The goal being that designers start to become "Component Engineers", in the same way that many "Data Analysts" have become "Analytics Engineers". [1]: https://discover.getdbt.com https://discover.getdbt.com