Decreased cross-site data availability times by 85%, deleted 10% of the codebase, and ended a series of upload failures by eliminating the system's tight coupling with aging, failing hardware. For our team, this intervention decreased the system's operational burden and laid the groundwork for a cloud-based migration. For our customers, data were available minutes after upload, rather than days.

Reduced rendering time by 99.8%. This decrease allowed engineers to examine previously un-plottable timeseries, e.g., timeseries with 12 million points, which rendered in less than 300 milliseconds.

Improved maintainability and reliability by modularizing the codebase and introducing tests on all state transitions. As a result, only three bugs were found during 1.5 years of daily use by hundreds of individuals.

Led transition from a restrictive, YAML-based DSL to a composable Python library by collecting customer feedback and gaining buy-in from stakeholders.