Summary
Yevhenii Soboliev is a Staff Big Data Engineer and team lead based in Seattle with nine years of experience designing and operating large-scale, cloud-native data platforms for enterprises. He specializes in distributed processing and orchestration—building reliable, high-throughput Spark, Kafka, Airflow, and Iceberg-based ecosystems across AWS and GCP, and has driven migrations such as YARN-to-Kubernetes orchestration at scale. Equally fluent in Scala and Python, he pairs hands-on coding (and earlier Java web experience) with CI/CD, observability, and container best practices (CKAD certified). He blends engineering leadership with security and governance expertise, implementing schema evolution, encryption/tokenization, and performance tuning for petabyte-class pipelines. Having split time between top-tier firms including Apple and Grid Dynamics and brief AI/LLM product work, he brings both platform-grade rigor and practical experimentation in AI-enhanced tooling.
9 years of coding experience
7 years of employment as a software developer
National Technical University "Kharkiv Polytechnic Institute"
English, Ukrainian