I think you'd better splitting before you do imputation. For instances, You want to impute missing value with column mean. In this case, if you impute first with train+valid dataset and split next, then you have used validation dataset before your model being built, which is how Data Leakage problem comes into picture.

But you might ask, if I impute after splitting, it may be too tedious when I need to do cross validation evaluation. My suggest for that is to use sklearn pipeline. It really simplifies your code, and reduce the chance to make a mistake. See Pipeline

I think you'd better split before you do imputation. For instances, you may want to impute missing values with column mean. In this case, if you impute first with train+valid data set and split next, then you have used validation data set before you built your model, which is how a data leakage problem comes into picture.

But you might ask, if I impute after splitting, it may be too tedious when I need to do cross validation. My suggest for that is to use sklearn pipeline. It really simplifies your code, and reducex the chance of making a mistake. See Pipeline