Yin-Yang sides of feature engineering.
We can combine tables, transform features to create new features. We can even do so with help. Let's see featuretools
After we created "meaningful" features, we are now ready to transform their format.
There are many available tools for this.
We can do this automatically using featuretools. Or we can remove them by hand.
This is a classic and fast method, but it has it's limitations. Remember standardize your data before you do this. We can do this using sklearn very fast.
We need sufficient data to do this well, more complicated than PCA.
Before the final task, we could try to solve a representive task. Use feature importance for a model, usually trees, to select features. Use SelectKBest (e.g. here). In addition, we can do selection while training - LASSO regularization etc.