The practical constraints that sustained the qualitative-quantitative divide in political science — limited data, inaccessible tools, and small samples — have been eliminated by the digitization of society, advances in computation, open-source movements, and generative artificial intelligence. Computational methods can now capture the contextual richness that qualitative scholars have long demanded, while the complexity of available data now requires quantitative scholars to engage with questions of theory, interpretation, and meaning. Yet capacity without coordination is wasted potential. Large-scale collaborative projects face learning costs (acquiring knowledge of the project) and attention costs (tracking what others are doing), both of which grow quadratically as projects expand — a problem generative AI exacerbates by increasing each contributor's output. This paper argues that Domain-Driven Design (DDD) combined with a Ports and Adapters programming architecture provides a framework for scalable collaboration that resolves this tension. DDD minimizes learning costs by aligning project structure with the substantive knowledge domain experts already possess, while Ports and Adapters minimizes attention costs by enforcing modularity and decoupling, allowing collaborators to work independently without unintended interference. Crucially, both approaches place qualitative expertise at the center of quantitative infrastructure: the domain layer is structured by substantive understanding, and technical implementations serve — rather than constrain — conceptual decisions. We demonstrate this framework through applications to electoral boundary fairness and legislative debate quality, showing how domain modeling and architectural separation enable resource-sharing across substantively distinct research areas. The advance of data and computing, we argue, means there can be no quantitative turn in political science without a qualitative turn in quantitative methods.