• Designed and implemented end-to-end ETL workflows to ingest, transform, and load large datasets into a centralized data warehouse.• Designed and implemented secure data storage solutions for managing PHI data at rest using encryption protocols (e.g., AES-256)• Ensured PHI data in transit was safeguarded using secure transmission methods such as TLS/SSL.• Led the architecture and migration of Structured data to AWS Cloud, reducing query latency by 60% and improving scalability.• Collaborated with stakeholders to define data requirements and establish data governance frameworks.• Managed and optimized data pipelines, ensuring data accuracy, security, and performance.• Introduced dimensional data modeling techniques to enhance analytics capabilities, enabling stakeholders to generate actionable insights.• Review ETL written by team members

• Implemented Agile practices and Scrum for continuous delivery and iterative improvement. • Cut deployment time by 50% of ETL Pipeline through containerization with Docker and optimized orchestration using Amazon ECS, Fargate and ECR • Designed and implemented data integration workflows using Azure Synapse Analytics, Azure Data Lake Storage, Azure Databricks, Azure Key Vault and Azure Data Factory, improving data accessibility by 30% for both structured and unstructured data • Shortened extraction time by 75% for On-Premises Clients by transitioning from sequential to batch processing with Dask for parallel computing. • Developed a serverless analytics product for over 5,000 US healthcare clients, streamlining data processing through AWS Glue and RDS. • Managed and automated ETL workflows using AWS Glue, standardizing client data and automating output to S3. • Increased report generation speed by 50% using Amazon QuickSight with daily data refresh via SPICE.

1. Led technical leadership efforts and mentored junior staff on database architecture and optimization.2. Optimized data distribution by analyzing shard keys for MongoDB, increasing performance and availability3. Created Python scripts to monitor MongoDB, achieving 99.99% availability.4. Resolved performance issues, reducing escalations by creating indexes and enhancing MongoDB operations.

1. Boosted team productivity by 50% by providing technical solutions for MS SQL Server issues.2. Automated processes through the development of stored procedures, reducing manual effort and improving efficiency.3. Handled performance tuning, database security, and optimization, ensuring efficient operation.

1. Collaborated with business analysts to understand database reporting needs, delivering efficient solutions.2. Enhanced SQL query performance by 5x, streamlining reporting and increasing system efficiency.3. Streamlined data loads and routine updates, contributing to system efficiency and reliability.