If you train the model E2E (not just fine-tune the task layer), it would modify the pre-trained parameters of all the layers (including the embedding layer).
However, remember the BERT embeddings are different from the word2vec embeddings and they depend on the context. Hence, they cannot be used as it is for a different task (unlike word2vec embeddings which don’t have context).
Until absolutely necessary to fine-tune the embeddings, you can fine-tune task layers (over BERT pretrained) model and adapt it to your specific problem set.