Nathan Fritter is a Senior Data Engineer with 10 years of experience building reliable ETL pipelines, data platforms, and cost-aware cloud solutions across companies from startups to Google and Lyft. Currently at Capital One, he brings deep hands-on expertise in Airflow, BigQuery, Redshift, and cross-cloud migrations, having rebuilt and optimized complex data workflows and led high-impact dataset migrations. He pairs strong technical delivery with stakeholder and project management skills—driving SLA adoption, scoping large migrations, and presenting findings to executives. A UC Santa Barbara Applied Statistics graduate, he has a track record of rapid domain learning (energy, payments, capacity engineering) and automating manual processes to save significant time and cost. Outside work he blends consultancy rigor with a practical outdoors-and-beer sensibility, and actively pursues machine learning and infrastructure improvements through self-led projects and certifications.
11 years of coding experience
4 years of employment as a software developer
High School Diploma (Advanced Scholar), High School Diploma (Advanced Scholar) at Dublin High School
Bachelor of Science (B.S.), Applied Statistics, Bachelor of Science (B.S.), Applied Statistics at UC Santa Barbara
Machine Learning, Machine Learning at Coursera | Stanford
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