Rashi Veershetty

Engineered SQL-based ETL pipelines for Power Pay application, handling 40+ business lines across 10+ products with unique payout logics, automating processing from Amazon S3 data ingestion to reduce turnaround time (TAT) for DSA/client payouts by 40%.

Developed scalable data transformation workflows to fetch, merge, and compute multi-file inputs (S3 CSV/Parquet) into unified datasets using Python/Pandas and SQL stored procedures, enabling real-time backend logic execution for complex product hierarchies.

Optimized data orchestration for production-grade payouts, implementing schema validation, incremental loads, and error-handling to ensure 99.9% data accuracy across diverse business requirements while maintaining compliance and audit trails.

Collaborated with business and dev teams to deploy CI/CD-integrated SQL changes via Git, monitoring pipeline performance with basic Airflow DAGs to handle high-volume financial datasets efficiently.

Developed Python-based automation frameworks for testing large-scale sensor datasets in Advanced Driver Assistance Systems (ADAS), reducing manual data validation efforts by 30% and enhancing accuracy in ETL-like processes for real-time data pipelines.

Introduced continuous integration (CI) pipelines using Python scripts, streamlining data transformation workflows and cutting critical data quality issues by 20% across the software development lifecycle.

Performed Software-in-the-Loop (SIL) testing on data processing code, debugging pipelines to ensure high-quality outputs, which decreased test execution time by 35% through optimized Python implementations.

Collaborated with cross-functional teams on code reviews, enforced data engineering standards (e.g., schema validation, data integrity checks), and delivered all pipeline components on schedule for production deployment.