Sajin Shrestha

About Sajin Shrestha

At Johnson & Johnson, contributed to the design and architecture of a scalable healthcare analytics platform in collaboration with architects and data scientists. Supported enterprise clinical and operational data initiatives by leveraging tools like Azure Databricks, Snowflake, Apache Spark, and Azure Data Factory. Developed secure data ingestion pipelines and storage architectures to enable reliable and efficient data integration and analysis. Previous experience at Biogen and Lifepoint Health® includes building data ingestion pipelines, designing structured storage layers using Amazon S3 and Redshift, and creating ETL workflows with tools like Python and SQL. Skilled in Amazon S3, Redshift, and CloudWatch, with a focus on data engineering solutions for healthcare and operational data.

Experience

1. Senior Data Engineer

   Johnson & Johnson

   Feb 2023 — Present · New Brunswick, NJ, US

   Led the architecture of a scalable healthcare analytics platform using Azure Databricks and Snowflake, working with architects and data scientists to support enterprise clinical and operational data initiatives.• Built reliable data ingestion pipelines using Apache Spark and Azure Data Factory to integrate EMR systems, partner APIs, and structured batch datasets into the enterprise data platform.• Designed secure storage architecture using Azure Data Lake Gen2 and Snowflake, implementing layered storage zones for raw, curated, and analytics ready datasets.• Collaborated with analytics and business teams to build trusted reporting datasets using Snowflake and dbt, enabling faster access to reliable operational insights.• Participated in architecture discussions to modernize legacy systems into a cloud native lakehouse architecture using Azure Synapse, Databricks, and Snowflake.• Implemented enterprise data governance controls using AWS Lake Formation and Snowflake security policies, ensuring secure access management and regulatory compliance.• Strengthened platform monitoring by implementing Azure Monitor and Databricks metrics, enabling better visibility into pipeline health and operational performance.• Optimized compute workloads through Snowflake warehouse tuning and Databricks cluster management, improving overall pipeline efficiency and system performance.• Implemented data validation frameworks using Great Expectations and Spark, improving data quality monitoring across ingestion and transformation pipelines.• Mentored junior engineers and contributed to architecture reviews for platforms built on Azure Databricks and Snowflake, promoting best practices for scalable data engineering.• Supported enterprise cloud migration initiatives by modernizing legacy ETL workflows into Azure Data Factory, Databricks, and Snowflake pipelines, improving scalability and maintainability.