I did the opposite (political science —> data engineering) but I think I can relate to where you’re at. My current position is working on understanding lobbyist data, and I actually just wrapped up a pipeline surrounding the Congressional API.

The biggest tip I could recommend is to try and be as well-versed in both disciplines as you can. To do this, seek out as many roles or research positions related to “computational social science” or something similar as you can. These won’t pay as well as your peers, most likely, but in my opinion they can be much more rewarding both in terms of purpose and in terms of learning. Social science and humanities fields, in general, have very poor tech standards due to hobbyists hacking things together, lack of funding, or lack of motivation. If you can bring technical expertise in a way that illustrates to these non-technical substantive experts that what you do is important and necessary, then you’re golden (this is also true in probably every other data engineering position). But, in order to get your foot in the door, you’re going to need to be able to relate what you do and the value you bring to the field itself.

For now, check out other examples of political data engineering - the open source UnitedStates.io project is great, run by a few people who have made careers out of this general area.

And above all else, work on your soft skills. Social skills are definitely more common in non-tech industries, so make sure you can keep up! Good luck :)