>a lot of the problems that data teams face in my experience are around how to expose data to the organization in a meaningful, regular, digestible, actionable way. This means thinking about how to ingest and transform data and present it to end users for analysis (BI) in such a way that it’s not painful or arduous to consume it, and ensuring that it is clean and accurate

100% this. I basically work in this role in my organization. I'm not sure how typical my experience is compared with other people who call themselves 'data Engineers'. I'm usually put into the situation where I'm looking at how to capture a raw signal (out of an instrument, device or similar) and package it up as structured data and store it in some kind of meaningful format people can use.

Useful skills I think (other than SQL) are: -Some understanding of PLCs/SCADA/OPC communication world. -knowledge about Signal analysis/ DSP, Kalman Filters, Fourier Transforms etc. Especially useful when you need to store a raw signal as "data" of some kind. also increasingly useful when the underlying data is in some format like Video, Audio etc. -An Understanding of working with Time Series analysis can help with data transformation etc (downsampling a high frequency signal into lower frequency one for example).

Once you have captured the signal and stored it you move on to the second part how to make it available to end users in a meaningful and accessible way - this is where SQL knowledge and working with DBAs comes into it. In my experience it has become increasingly common you need to understand about cloud architecture etc as well. For example in my org more and more of our storage is moving to Azure so knowing how to navigate that stack and all the terms they use for things can help save a lot of time and frustration.

Lastly it can help if you are at least somewhat familiar with the BI tools the end users will be using to ultimately leverage the data.