Nikhil Yalavarthi
Data Engineer @Optum
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WORK HISTORY
Data Engineer @Optum
Irving, TX, US
Built large-scale ETL workflows in Azure Data Factory and AWS Glue, processing terabytes of healthcare claims and EHR data daily. Developed data lakes on Azure Data Lake Storage and Hadoop (HDFS) to centralize patient and clinical datasets for analytics and ML. Orchestrated batch + real-time data pipelines with Apache Airflow and Kafka, reducing data latency from hours to minutes. Modelled healthcare datasets in Snowflake + Redshift, optimizing query performance and enabling HIPAA-compliant analytics. Implemented data quality frameworks in Python (Pandas + Great Expectations), reducing downstream anomalies by 25%. Automated infrastructure deployments using Terraform + Azure DevOps, cutting manual provisioning time by 40%. Integrated Grafana + Elasticsearch for system observability, enabling proactive monitoring and rapid incident resolution. Secured pipelines with HIPAA-compliant encryption, IAM, and PHI masking, ensuring end-to-end governance. Delivered Power BI and Tableau dashboards for population health, cost prediction, and clinical operations analytics. Partnered with data scientists to build predictive models using Scikit-learn + Azure ML, achieving model AUC ≥ 0.85. Operationalized predictive models using Azure ML + Airflow DAGs, automating scoring pipelines for population health analytics Developed a data quality framework with Python and Great Expectations, reducing downstream data errors by 30% Collaborated cross-functionally on data standardization and schema-mapping frameworks, improving interoperability across Optum’s enterprise platforms.
EDUCATION
Lewis University
Master's degree, Business/Corporate Communications
ABOUT NIKHIL YALAVARTHI
Multi-Cloud Data Engineer with 4+ years of experience building and optimising data pipelines, lakehouse architectures, and ML-ready platforms across Azure, GCP, AWS, and Snowflake. Skilled in Databricks, PySpark, Kafka, Airflow, and Terraform for end-to-end data modernisation and automation.At Citi, I design cloud-native pipelines using BigQuery, Dataproc, and Vertex AI, driving real-time fraud detection and analytics. Previously at Optum, I engineered healthcare data lakes and ELT frameworks in Azure ADF and AWS Glue, improving data quality and compliance at scale.Proven success reducing pipeline latency by 35%, maintaining 99.9% data availability, and delivering production-grade CI/CD and observability frameworks. Passionate about building reliable, governed, and scalable data systems that power decision-making and machine-learning innovation.Always open to connecting on data engineering, MLOps, or cloud transformation initiatives.
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