This paper provides a novel framework that learns self symmetric functional maps with an end goal of exploiting symmetry-aware representation as an embedding for non-rigid shape matching. In contrast to prior work in this direction, our framework is trained end-to-end and thus avoids instabilities and constraints associated with the commonly-used Laplace-Beltrami basis. On multiple datasets, we demonstrate that learning self symmetry maps with a deep functional map projects 3D shapes into a low dimensional canonical embedding that facilitates non rigid shape correspondence between unseen shape categories via a simple nearest neighbor search. Our framework outperforms all recent learning based methods on two partial shape matching SHREC benchmarks while being computationally cheaper.