The odds ratio is in a sense the “scale” where variables live in a logistic regression.
Whereas in a linear regression your can interpret a coefficient of say 1.5 as saying that for every increase in 1 unit of your predictor variable (e.g. years of education), there is a 1.5 increase in your predicted variable (e.g. raw income), in a logistic regression things are a bit different.
Here you are predicting binary outcomes (0-1, e.g., income above or below 50k) and an exponentiated coefficient of 1.5 says that for every increase in 1 unit of your predictor variable there is a 1.5 increase in the ”odds" (i.e, relative likelihood or p/(1-p)) of your predicted variable being 1 (ie income > 50k) instead of 0. Read another way, it says that for every extra year of education you are ~1.5x more likely to make > 50k than not, compared to someone with one year less of education.