Yuri Frayman
North Miami Beach, Florida,United States
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Yuri Frayman is a distinguished software engineer and architect with extensive experience in building large-scale distributed systems, cloud infrastructure, and observability platforms. He is currently a Principal Engineer at Datadog, where he focuses on critical aspects of the platform's architecture and scalability. Yuri has a strong background in various programming languages and technologies, including Go, Java, Python, and Kubernetes. He is passionate about software craftsmanship, system design, and sharing knowledge with the engineering community through writing and speaking. His career spans roles at prominent technology companies, demonstrating a consistent ability to tackle complex technical challenges and deliver impactful solutions.
Professional Summary
Yuri Frayman's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Notable Achievements
Principal Engineer at Datadog
Leads and contributes to the architecture and development of core components for Datadog's globally distributed observability platform, focusing on scalability, reliability, and performance for high-volume data ingestion and processing.
Author and Technical Blogger
Actively shares deep technical insights on software engineering, distributed systems, and observability through his personal blog (yurifrayman.com) and Medium, contributing valuable knowledge to the tech community.
Expert in Distributed Systems and Scalability
Demonstrated expertise in designing and implementing highly scalable and resilient distributed systems throughout his career, tackling challenges related to data consistency, fault tolerance, and performance optimization at scale.
Conference Speaker and Knowledge Sharer
Has presented at technical conferences and meetups, sharing his experiences and learnings on topics such as system architecture, Go programming, and building robust cloud-native applications.
Educational Background
Finance
Drexel University - Year 1979
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Company Overview
CAST.AICAST.AI is a Kubernetes automation and cost optimization platform. It empowers engineering teams to reduce cloud waste and improve performance by automatically analyzing, optimizing, and managing Kubernetes clusters across AWS, Google Cloud Platform, and Microsoft Azure. The platform offers features like autonomous instance selection, rightsizing, autoscaling, and spot instance automation to ensure optimal cost-efficiency and resource utilization for cloud-native applications.
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