Phanender Chalasani

Data Engineer with 5+ years of experience designing and deploying large-scale data platforms and real-time analytics systems across cloud-native environments. I specialize in building production-grade ETL/ELT pipelines, dbt Core transformation frameworks, and MLOps workflows that transform raw data into actionable insights. Currently at Optum (UnitedHealth Group), I architect enterprise-scale data and ML pipelines processing 2B+ healthcare records monthly — delivering curated datasets that power BI dashboards, predictive models, and ML/AI workloads with 99.9% pipeline uptime. Previously, I worked as a Graduate Research Assistant at Texas A&M University, building cloud-based data platforms for agricultural and satellite imagery research using Python, PySpark, and Apache Kafka. Before that, as a Software Engineer at RecVue, I developed backend data integration services and ETL pipelines for enterprise billing platforms serving 100K+ users. I hold a Master's in Data Science (GPA: 3.8) from Texas A&M University and multiple cloud certifications, including Google Cloud Professional Data Engineer, AWS Machine Learning Engineer Associate, Databricks Data Engineer Associate, and Azure AI Engineer Associate. I enjoy solving complex data engineering challenges, optimizing cloud architectures, and bridging the gap between data engineering and machine learning. Always open to connecting with professionals in Data Engineering, Cloud Architecture, and MLOps.