Nathaniel Young is a data engineer based in San Francisco with nine years of experience building scalable data pipelines, automation, and NLP-backed data quality tooling. He has driven high-throughput ETL and validation systems at Apple and Sayari, optimizing workflows with Airflow, multithreading, streaming, Docker/Kubernetes, and cloud storage for workloads that include terabytes of imagery and video. Comfortable across backend APIs, schema design, and distributed processing, he also brings product-minded full‑stack experience from earlier roles automating marketing data enrichment and building custom ML-backed text-cleaning services. A Purdue CS graduate, he blends hands-on engineering with a knack for finding and automating repetitive data problems—often surfacing subtle data-quality issues via custom validation, EXIF checks, and semantic heuristics.
9 years of coding experience
1 year of employment as a software developer
High School Diploma, 4.2, High School Diploma, 4.2 at The King's Academy
Bachelor of Science (BS), Computer Science, 3.12, Bachelor of Science (BS), Computer Science, 3.12 at Purdue University
My contributions to Apache Airflow. See my non-main branches or the upstream repo.
Contributions:32 pushes, 6 branches in 9 months
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