I work as both, Veronika. I’d venture:

An average data engineer barely understands what information means formally, and often understands data storage without fully understanding how critical the details of data capture is in the value of data as information.

A knowledge engineer is often trying to capture what humans call knowledge without any understanding of how it arose. Virtually nothing that is done in knowledge acquisition, representation, system design, validation or maintenance has anything to do with what makes knowledge knowledge rather than data curated and assembled as reusable information.

So I think they have the data engineer’s problem, only worse.