Multi-categorical variable in linear regression


I am trying to explain the variance in "wages" across different groups of the population. In my model, "wages" is a dependent variable. The key independent var is "groups" (Non-immigrant is the reference group). Then I add several controls such as age, education, occupation, etc. However, I have an issue with the occupation variable. In my dataset, there are several occupation variables that I can use:
(1) 10 categories of occupation: Management, Sales, Business, Health, ...
(2) Skill levels of occupation (5 categories): Management, Professional, Technical, Intermediate, Labor
(3) 40 categories of occupation (which is kind of a combination of (1) and (2)): Senior manager, middle manager, ...

My questions:
1/ I want to include both (1) and (2) in my model because both of them (occupation groups and skill levels have a strong impact on the variance of wages. However, there is one overlapping category (Management). How can I include both of them into the model without any issue?
2/ In case I can't include (1) and (2) in the same model, is it okay if I include all 40categories (actually 39 because 1 is committed as a reference group) into the model? I use the Census 2016 so the sample size is quite large. But I am still worried that too many categories may constrain my model because in addition to occupation, I also have levels of education, age categories and some other categorical variables as well.

Thank you very much for your answers in advance! Your help is much appreciated!