Santona began her data journey through fundamental physics—searching through massive event data from particle collisions at CERN to detect rare particles. She’s since extended her machine learning engineering to natural language processing, before switching focus to product and data engineering for data workflow authoring frameworks. As a python engineer, she started with the programmatic data orchestration tool, Airflow, helping improve its usability for data science and machine learning pipelines. Currently at Upsolver, she leads data engineering and science, driving research for the declarative workflow authoring framework in SQL. Santona is passionate about building, as well as empowering others to build, end-to-end data and ML pipelines, scalably.