The article doesn't mention two important things (and instead focuses on being clever - the opposite of what machine learning stands for). First, the deep learning algorithms that automatically create features. Second, the importance of gathering lots of data, or generating it.
If you have to be really clever with feature engineering, then what's the point of even calling yourself a machine learning person.
I agree that deep-learning is an interesting approach to learn higher level features. However it's still a long way from being a universal solution: for instance deep learning won't help you solve the machine translation or multi-documents text summarization problems automagically: you still need to find good (hence often task dependent) representation for both your input data and the datastructure you are trying to learn a predictive model for.
Deep learning is an interesting approach - although the features that DL algorithms decide are most important are not always intuitive or weighted properly in context. Partial-feature engineering is sometimes the only way to effectively deal with biases, especially in higher-dimensional space where the DL features can be very opaque.
If you have to be really clever with feature engineering, then what's the point of even calling yourself a machine learning person.