• Data Pipeline Architecture: Designed and managed data pipelines using Hadoop and Spark, improving processing efficiency by 30%.
• Data Integration: Created and enhanced data solutions for seamless data flow across the ecosystem, leveraging open-source tools and data-processing frameworks.
• ETL Processes: Developed and optimized ETL processes using Python and PySpark, ensuring efficient data extraction, transformation, and loading.
• Business Analysis: Provided business analysis and developed ETL code to meet technical specifications and business requirements.
• Data Governance: Implemented data governance policies including data lineage, quality checks, and classification to ensure data integrity.
• Cloud Technologies: Utilized AWS services (Lambda, S3, DynamoDB) for various data-driven solutions, enhancing data accessibility and reliability.
• Custom Data Pipeline Development: Developed custom data pipelines for both cloud and locally hosted environments, ensuring data consis

• ETL Development: Developed and managed ETL processes using microservices, improving data extraction, transformation, and loading efficiency.
• Machine Learning Models: Built and deployed machine learning models using Sklearn and SparkML, improving model performance by 20%.
• API Services: Created and deployed production-ready API services for real-time model evaluations, ensuring high availability and quick response times.
• Data Analysis: Analyzed EMR and claims data to support healthcare decision-making, providing actionable insights.
• Cross-functional Collaboration: Worked closely with data engineers, product managers, and business analysts to refine data processing workflows and improve overall system performance.
• Data Quality Assurance: Implemented data validation processes, reducing data errors by 25%.