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Data-driven models are emerging as complementary approaches to numerical methods across the Earth sciences, offering the potential to be computationally efficient and free from physical parameterization bias. We present a neural network trained only on observations, integrating spatially and temporally sparse data from various altimeter instruments, to predict a dense reconstruction of water levels for the Amazon River network. Dense estimates for water level can enable better parameterizations of hydraulic models as well as accurate modeling of discharge in small- to medium-sized catchments.The Amazon basin hosts the largest rainforest in the World, making the monitoring of its rivers particularly important. Historical records of water level rely on in-situ flow gauges maintained by basin authorities (e.g. ANA in Brazil), offering temporally dense but geographically sparse observations. Since the 1990s, satellite altimetry has provided global yet sparse observations in ungauged areas. The recent SWOT mission introduces unprecedented spatial density thanks to its wide-swath InSAR sensor but lacks historical depth. To synthesize these disparate sources into a homogeneous product, we train an attention-based graph neural network for spatial and temporal densification via masked reconstruction. The model is trained to predict SWOT measurements conditioned on classical altimetry for 2023-2025, so it learns to infer the denser measurements taking only classical altimetry as input. River topology information from the SWORD database determines the decoding order and sparsifies attention interactions in the model architecture, with the aim of learning spatiotemporal dynamics in a physically consistent manner.We empirically validate the model on spatially and temporally held-out evaluation sets that include in-situ measurements from ANA gauges and benchmark it against an existing hybrid statistical-physical approach. We predict a first version of a reconstruction consisting of daily water level estimates for every SWOT reach in the Amazon basin between 2000 and 2025. This study contributes to the development of neural networks that unify sparse, non-overlapping sensor data without relying on physical approximations. In the future we will integrate complementary observations such as river width derived from imagery or SAR, and extend the framework to other major river basins globally.