Daniel Song is a Senior Customer Success Engineer with 11 years of hands-on experience building secure, automated cloud platforms across Azure, GCP, and AWS. He has designed multi-region GenAI foundations, Terraform-driven cloud foundations, and Kubernetes/Serverless pipelines that prioritize security-as-code, observability, and repeatable patterns. At EY he helped a 60+ engineer org ship a production Generative AI platform with shift-left security and cross-region automation, and earlier work spans GovCloud deployments, CDC pipelines to BigQuery, and daily UI testing automation. Daniel focuses on platform-level problems—multi-region resilience, PR-based compliance guardrails, and test automation—to make the safe path the default for teams. Based in San Diego and Carnegie Mellon-trained, he blends consulting rigor with hands-on engineering and a knack for translating complex compliance and operational needs into developer-friendly automation.
11 years of coding experience
5 years of employment as a software developer
Information Systems, Information Systems at Carnegie Mellon University
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.