Marko Herkaliuk is a data engineer with eight years of experience and a strong data science foundation, currently building data infrastructure at Lyft from Kyiv. He designs and deploys end-to-end ML systems—covering requirements, hypothesis testing, training, validation, production deployment, monitoring, and automated retraining—while also implementing ETL/ELT pipelines, DWH/lakehouse architectures, and feature stores. His background spans applied physics to practical ML impact, including uplift modeling in fintech that cut collection costs by up to 20% and production ML services at scale. Comfortable with cloud-native tooling (Terraform, ArgoCD), orchestration (Airflow), and data ingestion (Airbyte), he blends hands-on engineering with attention to operational metrics and model obsolescence. Notably, he focuses on making data platforms convenient for analysts and scientists, turning complex pipelines into reliable, observable systems.
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