Fundamentals. How networks work, e.g http, TCP/IP, UDP, icmp. Basic security, SQL injection, XSS, CSRF. SQL databases, normalization, indexes for queries. Application and implications of distributed concensus algorithms. Program decomposition and composition. Big-O time/space algorithm efficiency. If you know these things in any context they can all be readily applied in another.
Storing and retrieving data, processing data and communicating between systems is probably the fundamental parts of most business systems. If you've done this using a few different technologies there should be few major surprises except RTFM and Stack Overflow (!)
Obviously fundamentals evolve slowly. The data structures, OS, and programming language paradigms I learned 20 years ago can still help me today. In data science, most problems are solved by solid, fundamental stats work and a solid grasp of the scientific method.
data science. Or, what's essentially a catch-all term these days for everything from sql queries to Artificial intelligence. The tools, applications have of course tremendously evolved but at its core, if you develop a good eye for working with data (read recent blog by the ex-Spotify guy), you'll find yourself in surprising career moves and growth.
- source: data point of one, my own anecdotal account of being in data for the last 16 years
It's not glamorous but SQL comes to mind first when I think about this. Compared to the mountains of business-crtitical code written in SQL the number of people who are really good at it is tiny.
