Regression with categorical variable as dependent variable and mixed independent variables along with presence of multicollinearity

So I have this data of a company who is facing high attrition rate. So I am trying to find out the significant variables on which Attrition depends. So for that, I did logistic regression with Attrition rate as a dependent variable. But the p values for all of them are coming out to be around 1. ( I guess that is because of multicollinearity. For this I checked multicollinearity with correlation matrix and removed some variable also, but still the p values are zero. I also did VIF to select the variables with values less than 10, but even then the p values of the model are around 1.)

Please help me with this. (I did check if the categorical variables are "factors")

fit1<-glm(Attrition~Age+BusinessTravel+Distance.From.Home+Education.Field+Gender+
Job.Satisfaction+Marital.Status+Over.Time+
Percent.Salary.Hike+Performance.Rating+Relationship.Satisfaction+
Total.Working.Years+Years.At.Company+Years.In.Current.Role+Years.Since.Last.Promotion+Years.With.Curr.Manager, family = "binomial")


dput

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