How can I add custom numerical features for training to BERT fine tuning?
With BERT I am assuming you are using finally the embeddings for your task.

Solution 1: Once you have embeddings, you can use them as features and with your other features and then build a new model for the task. 

Solution 2: Here you will play with the network. 



Now here left one is the normal BERT, in the right we have another MLP network to deal with other numeric features, on top we are combining embeddings from both and then passing through one more MLP to give the final output. 
Here the advantage is, while backpropagation, the BERT weight updates will have some contribution because of the loss due to numeric features.

As you mentioned you are new to TF and Bert, refer https://towardsdatascience.com/bert-in-keras-with-tensorflow-hub-76bcbc9417b to see how BERT can be implemented, and with this only doing the tweak to create suggested network will be easy. 
Feel free to reach out to me in case of any further questions.