Increasingly complex autonomous systems involve different types of models and large volumes of data–both coming from disparate communities. Scientific, engineering, and machine learning models are traditionally quite different in format, granularity, time-scale, use, and so forth. Furthermore, these disparate model types make use of heterogeneous data–for instance, time-series data and cross-sectional environmental data, which may be incompatible. As system complexity and data volume both increase, integrating these heterogeneous models and data in a consistent and systematic fashion becomes challenging. Furthermore, disparate disciplines and fields of study perceive and use models differently. One community attempting to use models from another community may not only miss out on their benefits, but may even misuse the models (e.g., by overlooking their limitations or misunderstanding their assumptions). This talk will explore the roles that the various model and data types play in a system’s development. It will also discuss strategies for bridging the gap between model-driven engineering and data-centric autonomous systems development. We will overview MODA, a recently proposed unified conceptual reference framework intended to serve as a scientific foundation for data-centric and model-driven systems engineering. We will illustrate the MODA framework in terms of autonomous systems that we have developed with our collaborators. These instantiations and others have helped us to identify research challenges when using an engineering-based approach to develop autonomous systems that integrate heterogeneous models and data.