Vasanth Manne

Background

About Vasanth Manne

I am a highly accomplished Data Engineer with over three years of experience in designing, building, and optimizing scalable data solutions across diverse industries, including healthcare (HIPAA-compliant data), finance, and retail. I specialize in the full data lifecycle, from initial ingestion and complex transformation to the creation of analytics-ready datasets for BI and ML applications. My core technical stack centers on Python, SQL, PySpark, Databricks, Azure Data Factory, and advanced data modeling concepts like Delta Lake architecture. My professional track record demonstrates a consistent ability to deliver significant improvements in efficiency and performance. I engineered fault-tolerant pipelines processing terabytes of claims data and automated ingestion from various sources, which successfully reduced manual intervention by 30%. I have hands-on expertise in cloud migration, having contributed to the modernization of a large-scale platform by moving analytical workloads from AWS to GCP, and optimizing processing efficiency using techniques like Z-ORDER and partitioning in Databricks. Furthermore, I've implemented predictive models using Scikit-learn and MLflow to drive better business outcomes, such as optimizing patient eligibility and recruitment strategies. I am proficient in leveraging the modern data ecosystem, including orchestration with Airflow and Cloud Composer, and implementing robust data quality frameworks using tools like Azure Monitor and Log Analytics. My technical fluency spans all major clouds—Azure (Synapse, ADLS), GCP (BigQuery, Pub/Sub), and AWS (S3, Lambda) —ensuring I can design and manage sophisticated lakehouse architectures and support high-volume dashboards and regulatory reporting requirements with speed and reliability. I hold a Master's in Data Science from the New Jersey Institute of Technology.