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Built and automated an ETL pipeline using Python APIs integrated into Azure DevOps, saving ~7 hours of manual work per week. Built an on-demand Python pipeline in Linux to generate detailed metadata reports, contributing to a long-term client relationship. Optimized an Azure Function using Python, reducing storage costs by 15% and improving report execution time by 30% through automated processes.
Maintained daily accessibility of 40 TB of warehouse data in a cloud-based CEPH environment using Hadoop and PySpark. Automated anomaly detection in temperature and voltage data with PySpark, reducing manual validation time by 20% and improving detection accuracy by 10%. Bridged non-technical stakeholders and third-party engineers, improving issue resolution time by ~20% through clearer communication.
