The QueryTree framework is an abstracted approach to query construction and execution that enables the efficient and accurate generation of data for machine learning models. It re-imagines logical components of a query as nodes within a tree-like structure, allowing for enhanced readability and maintainability. The primary workflow involves building the tree, generating the compute-specific query, and executing it on compute infrastructure. This framework has enabled significant improvements in training dataset generation speeds, reduced data skew, and improved performance optimizations such as window function implementation of ASOF joins and join optimization. It also facilitates interactive debugging and analysis of Tecton-defined queries without requiring deep knowledge of underlying code.