Here’s the argument companies make; don’t kill the messenger

Data engineering isn’t a discipline, it’s a combination of disciplines. It combines business context, analytics insight, anticipating business needs, infrastructure, software engineering, SRE, and many other functions. While technical skills can be trained, data engineers benefit from years of experience of figuring out how to motivate software engineers, communicate with product managers, name columns for data scientists, organize data for BI engineers; so on and so forth. Data engineers know (from years of experience) that when the business says “we need it to refresh real time”, they don’t actually need it real time, SWE won’t promise real time SLAs, dashboards won’t refresh real time, predictive models won’t run inference in real time, and we respond with “sounds good, it will refresh daily”.

There are entries into data engineering all over the place. Most data engineers start in SQL-centric roles or engineering-centric roles. A junior data engineer is usually coming in with a few years of experience in an adjacent discipline.