Does shuffling the training data matter in a K Nearest Neighbors Classifier model?
If you are talking about K-nn algorithm per se, then, you do not have any advantage in shuffling your training set. Indeed, as you said, K-nn is just looking for the K nearest neighbors and does not care at all about the order of the samples, the algorithm will scan the entire training set for a single prediction, no matter what (unless you are using some efficient implementation like ball-tree or kd-tree).
Regarding the second point of your question, I am wondering what your pipeline is: are you augmenting the dataset before splitting in train-validation-test? If so, I think you are injecting some bias in the evaluation of you algorithm, because you are evaluating your algorithm in samples that do not exist. You should augment only the training set and then validate/test your algorithm on real data.