Led the preparation of of ETL data pipelines, optimizing data ingestion and transformation of insurance data using Apache Spark within AWS Glue and AWS EMR, improving processing efficiency by 30% through various Spark optimization techniques.

Developed Spark scripts using PySpark and SparkSQL for handling null values, data masking, standardization, and metric calculations, reducing data processing errors by 25% and enhancing data quality.

Utilized AWS Glue to discover and catalog data from AWS S3, establishing a central metadata repository for data schemas and locations.

Configured AWS EMR to execute PySpark scripts for transforming claims data, employing Spark optimization techniques to efficiently load Parquet-formatted data into Snowflake, leading to a 40% reduction in data loading times and improved query performance.

Worked closely with data analysts, business stakeholders, and AWS administrators to document transformation scripts, job configurations, and data handling practices, ensuring alignment across teams.

Conducted comprehensive analysis of insurance data in Snowflake and PostgreSQL, identifying key trends, patterns, and outliers. Created dashboards comparing current monthly data with the same month in previous years, driving a 15% improvement in claims management and supporting strategic decisions.

Designed interactive dashboards in Tableau to visualize key performance indicators (KPIs) and metrics related to insurance operations. These dashboards facilitated data-driven decision-making and increased stakeholder engagement by 25%.

Generated regular and ad-hoc reports, conducting statistical analysis and data modeling. This work derived actionable insights and recommendations for operational improvements, leading to a 10% enhancement in operational efficiency.