Data scaling before or after PCA
I once heard a data scinetist state at a conference talk: "Basically, you can do what you want, as long as you know what you are doing." 

This also applies here. The more statistically sound way would be to transform all variables prior to additional steps such as PCA or factor analysis. Then you still know the scale of your variables and can interpret the rescaling in the context of your application. If you have no such interpretation, but good reasons for rescaling your principal components due to computational issues arising if some values are to close to zero while others are quite large, rescaling the components makes sense. However, reversing this process and still being able to interpret the effect of the rescaling operation in your context will become almost impossible.