Tim Castillo
About Tim Castillo
Tim Castillo is a Data Engineer and Developer Advocate at Dagster Labs, where he focuses on data maturity and workflow automation. He has a background in software engineering and analytics, with experience at companies such as Roche, IBM, and Brooklyn Data Co., and holds a Master of Science in Health Informatics from the University of Denver.
Work at Dagster Labs
Tim Castillo currently serves as a Data Engineer and Developer Advocate at Dagster Labs, a position he has held since 2023. In this role, he engages with the data community, sharing insights on data maturity and workflow automation. He emphasizes the importance of infrastructure-as-code in integrating various SaaS platforms. His work involves advocating for best practices in data engineering and contributing to discussions on orchestration and scaling DAG visualization for enterprise-sized data asset graphs.
Previous Experience in Data Engineering
Before joining Dagster Labs, Tim Castillo worked at Brooklyn Data Co. as a Senior Analytics Engineer from 2021 to 2023. He also held positions as a Software Engineer at Roche from 2018 to 2020 and at IBM from 2017 to 2018. His experience includes a focus on ETL/ELT processes and the culture of learning and sharing within data engineering. Additionally, he worked as a Computer Science Tutor and Teaching Assistant at the University of San Francisco from 2014 to 2016.
Education and Expertise
Tim Castillo earned a Master of Science in Health Informatics from the University of Denver, completing his studies from 2020 to 2023. He also holds a Bachelor of Science in Computer Science from the University of San Francisco, where he studied from 2013 to 2016. His educational background provides a strong foundation for his work in data engineering, particularly in the areas of SQL, Python, and data pipeline development.
Contributions to Data Engineering Discussions
Tim Castillo actively participates in discussions surrounding various aspects of data engineering. He has contributed insights on the role of AI and LLMs in data, the importance of internal access and collaboration, and standardizing pipelines with domain-specific languages. His discussions often focus on engineering abstractions, iterative process improvement, and the journey from engineer to CEO, sharing lessons learned along the way.