Actually, thanks to your message I was thinking about it today and realized that you are indeed right. You are right that one variable by position is not the right approach. I actually need 5 indicator variables (one for each position). Or I could just do one model per position, but that leaves no way to test for the significance of position.
The standard practice for cathegorical variables is to include (n-1) dummies. In this case, we' need 4 variables (think of them as describing the "offset" from a base category).
This is only appropriate when you believe the position difference is a fixed effect, and the coefficients of other variables are identical. If you have reasons to believe that your categorical variable also affects the coefficients of other variables, you need to add interactions f all your LHS variables with all categorical variables.
You will still need a lot of observation, because the model now has a lot of variables (12 skill + 36 interactions + 4 dummies). At least 100 datapoints will be advisable.
As for regressing using current salary as a regressor, this again leaves potential as a y-variable. And in that case, if there are sub-levels on potential (ie: error), it still leads to a biased model.
You can try to flip the model, and regress salary on potential. If there is unexplained variance, this will indicate a sublevel in the potential -- though to me the results are pretty much a foregone conclusion, given that one can pretty much see that the same potential may lock at different salariy without running any parametric tests.
"I don't know half of you half as well as I should like; and I like less than half of you half as well as you deserve."