• Orchestrated the deployment of Amazon EC2 instances and skillfully managed S3 storage, customizing instances for specific applications and Linux distributions.• Offered expert insights on AWS system architecture, implementing rigorous security measures and protocols to safeguard vital assets and ensure compliance.• Optimized data processing using Amazon EMR and EC2, enhancing system performance and scalability.• Designed and fine-tuned data processing workflows utilizing AWS services like Amazon EMR and Kinesis, ensuring efficient data analysis.• Leveraged AWS Glue for data cleaning and preprocessing, performed real-time analysis using Amazon Kinesis Data Analytics, and harnessed services like EMR, Redshift, DynamoDB, and Lambda for scalable data processing.• Implemented robust data governance and security protocols through AWS IAM , ensuring protection of sensitive data.• Utilized AWS Glue and Fully Managed Kafka for efficient data streaming, transformation, and preparation.• Established and managed data storage on Amazon S3 and Redshift, ensuring accessibility and organization of data.• Seamlessly integrated Snowflake into the data processing workflow, elevating data warehousing capabilities.• Evaluated on-premises data infrastructure for migration opportunities to AWS, orchestrated data pipelines via AWS Step Functions, and employed Amazon Kinesis for event-driven processing.• Proficiently utilized Apache Airflow for workflow automation, orchestrating complex data pipelines and automating tasks to boost operational efficiency.• Fine-tune Kafka configurations to optimize performance, throughput, and latency.

• Cleaned and manipulated 40M+ Medicare Members dataset using Python, resulting in a 30% increase in data accuracy.• Developed comprehensive machine learning models utilizing advanced techniques such as XGboost, and ensemble learning to predict clients' medication adherence risk levels with an impressive accuracy rate of ~90%.• Utilized advanced data analysis techniques to identify key factors impacting CVS's STAR ratings, leading to the implementation of targeted interventions that led to a 10% increase in overall rating scores.• Conducted extensive data mining and statistical analysis to identify key factors influencing medication adherence, resulting in the development of targeted outreach methods predicted to improve patient compliance by 15%.• Worked on publishing Power BI reports on dashboards in Power BI server.• Worked with both live and import data into Power BI for creating reports.• Developed reports summarizing patient and call data trends, providing actionable insights to senior leadership.

• Collected and integrate data from various sources, ensuring accuracy and consistency. • Managed databases and data systems, including fixing coding errors and other data-related problems. • Performed detailed data analysis to identify trends, patterns, and insights that can help in decision-making processes. • Generated regular reports and ad-hoc analyses to present to management, highlighting key findings and recommendations• Developed statistical models to forecast future trends and provided insights that support business objectives. • Used predictive modelling to increase and optimized customer experiences, revenue generation, ad targeting, and other business outcomes. • Worked closely with various departments, including marketing, sales, and product development teams, to support their data analysis needs and inform their strategies.• Collaborated with stakeholders to understand their requirements and deliver data-driven insights and solutions• Created and maintain dashboards and visualizations to help stakeholders quickly understand the data and its implications. • Utilized tools like Tableau, Power BI to make data accessible and understandable to non-technical users.