Swati Priyadarshani

Developed and maintained scalable ETL pipelines using Python and Spark, resulting in 25% reduction in data processing time. Mould and implemented efficient batch data processing workflows using Airflow, increasing data processing capacity by 40%. Enhalnced and deployed data transformation scripts to clean and validate raw datasets, achieving a 90% data quality improvement rate. Solved complex SQL queries, improving performance by 30% and reducing query execution time by 50%. Design and implemented data warehousing solutions for structured analytics, resulting in a 25% increase in data insights and business decisions. Managed and maintained AWS cloud storage (S3) and Redshift data warehouse, ensuring 99.99% uptime and 50% reduction in storage costs. Collaborated with cross-functional teams to understand data requirements, resulting in a 30% reduction in data-related project delays and a 25% increase in data-driven business decisions.

Created data processing time by 30% and increased data storage capacity by 25% with scalable AWS-based data workflows. Boosted business intelligence by 40% and reporting efficiency by 35% with complex SQL queries. Increased customer engagement by 25%, sales revenue by 20%, and lower sales forecasting errors by 15% with AI-driven dashboards.

Used Python and SQL to design and implement efficient ETL pipelines for structured dataset processing. Developed and automated data validation scripts, resulting in a 40% reduction in manual effort. Provided support in data preprocessing for AI/ML applications, ensuring high-quality data readiness.