Narendra Papasani

Highly experienced Data Engineer with over 12 years of expertise in designing and implementing scalable data solutions using modern cloud and data warehousing technologies. Proven ability to deliver high-performance data pipelines, real-time analytics, and data governance frameworks across industries such as finance, healthcare, and retail. Adept at building robust ETL processes, streamlining data operations, and optimizing cloud infrastructure to enhance business decision-making.

Azure Snowflake Data Engineer - SENTARA NYC

(2024-08)

• Designed and implemented scalable data pipelines using Azure Data Factory (ADF) and Azure Databricks (PySpark), processing large-scale healthcare datasets from EHR and clinical systems.
• Built and optimized ETL/ELT workflows using ADF, Databricks, and Snowflake, improving data processing efficiency and reducing turnaround time for clinical reporting.
• Developed real-time data ingestion pipelines using Azure Event Hubs and Databricks Structured Streaming to process patient and operational data with low latency.
• Transformed and managed healthcare data in Azure Data Lake Storage Gen2 using Delta Lake (ACID transactions, schema enforcement, time travel) to ensure data reliability and compliance.
• Implemented data governance and security frameworks using Microsoft Purview, Unity Catalog, and Azure Key Vault, ensuring HIPAA-compliant handling of sensitive patient data.
• Developed modular and reusable DBT models in Snowflake, enabling automated transformations, data lineage, and consistent analytics across reporting layers.
• Optimized complex SQL queries, stored procedures, and views in Azure Synapse and Snowflake, reducing query execution time and improving reporting performance.
• Built real-time analytics solutions and dashboards in Power BI using DAX and DirectQuery, enabling actionable insights for patient care and operational efficiency.
• Automated CI/CD pipelines using Azure DevOps and Git, integrating testing frameworks for reliable and consistent deployment of data workflows.
• Engineered incremental data loading pipelines using Delta Lake MERGE operations, ensuring efficient updates and accurate historical tracking of patient records.
• Performed data profiling and validation using Python (Pandas, NumPy) in Databricks Notebooks, ensuring high data quality and integrity across pipelines.
• Integrated external healthcare APIs and third-party systems into Snowflake using Python, enabling unified and real-time data access.
• Designed data migration solutions to move legacy healthcare data into Azure and Snowflake platforms, ensuring seamless transition and minimal downtime.
• Implemented monitoring and alerting systems using Python scripts and Azure services, improving pipeline reliability and reducing failures.
• Integrated and automated data

Master's - computer science - University of Texas at Dallas (2013-12)

Bachelor's - computer science - GITAM University, Visakhapatnam, India (2012-05)