In this paper, we propose a deep neural network architecture to estimate physical and optical properties of water bodies from bathymetric lidar waveforms. Although essential to the understanding of coastal and inland waters dynamics, this task remains challenging due to the complexity of waveform processing. Here, we use convolutional encoders to estimate seven parameters without the need for pre-processing, iterations, or existing measurements of the target properties. Using a data simulator based on radiative transfer models, the network is trained to be robust to a wide range of physical and acquisition settings. On simulated data, the results show the ability of the method to retrieve relevant Kd, depth, and bottom position estimates even for low signal-to-noise ratios in which the water bottom component is particularly weak and the water column difficult to identify.