Growing social and economic pressures demand technological innovations that enable the widespread usage of unconventional sources of water (e.g., seawa- ter, grey water). This motivates the emerging fit-for-purpose paradigm, wherein water is provided at the precise quality level of the intended application. Un- fortunately, to date, the fundamental advances in materials and nanosystems engineering have been slow to advance this paradigm. We highlight the critical need to bridge scientific research at the molecular and nano-scales and tech- nology development at the device and systems scales for the implementation of sustainable fit-for-purpose water infrastructure. Specifically, we present four pil- lars from computational and data sciences to bridge between scientific research and technology development, namely superstructure optimization, model-based design of experiments, inverse material design, and uncertainty quantification. As such, we highlight opportunities to collaboratively revolutionize water sus- tainability engineering, but emphasize open communication between data scientists and water-focused researchers using a common vocabulary as a significant hurdle.