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Recovering missing sensor data is a critical problem for sensor networks, especially when nodes duty cycle their activity or may experience periodic downtimes due to limited energy. Fortunately, sensor readings are often correlated across different nodes and sensor types. Among state-of-the-art statistical data estimation techniques, latent variable based factor models have emerged as a powerful framework for recovering missing data. In this paper we propose the use of latent variable models to estimate missing data in heterogeneous sensor networks. Our model not only correlates data across different sensor locations and types, but also takes advantage of the temporal structure that is often present in sensor readings. We analyze how this model can effectively reconstruct missing sensor data when the individual sensor nodes have to duty-cycle their activity in order to extend network lifetime. We evaluate our model on a real life sensor network consisting of 122 environmental monitoring stations that periodically collect data from 13 different sensors. Results show that our proposed model can effectively reconstruct over 50% of missing data with less than 10% error.