Reading suggestions to improve my feature engineering skills?

Hello, /r/machinelearning!

I'm at a point in my continuing education of machine learning and data science where I feel comfortable with the mathematics and properties behind most models, and I feel competent in understanding when to choose certain models and how to transform and pre-process data so as to take advantage of the model's properties.

While I've been told this from the beginning, I recently had the Aha! realization that good features are really what separates good models from great models.

So my question to you all is, what books or papers should I read in order to get better at hand crafting features? I understand that this is sort of where the science meets the art, and that feature engineering is naturally pretty domain specific. But surely there are general intuitions that I could learn that could help me to build good features for domains I do understand?