Artificial neural networks showed astonishing results in the last decades. However, they tend to consume large amounts of energy which is problematic on edge devices such as wearables where the energy budget is limited. We propose a hybrid neural network of convolutional layers and spiking neural networks that combines the feature extraction capabilities of CNNs with the energy efficiency of SNNs for low-power radar signal processing. This approach is applied to our own radar-based gesture recognition dataset and the publicly available Soli dataset. The hybrid neural network achieves competitive or better accuracies on both datasets in comparison to similarly sized spiking and traditional networks. Experiments on the SpiNNaker 2 system show that they are a little less efficient with noisy input data compared to standard SNNs. The proposed network architecture is a straight forward approach for low-power signal processing of radar data.