Designed and operated high-throughput, event-driven distributed systems using Kafka and Springboot for processing millions of records daily with low-latency(< 100 ms), fault-tolerant data pipelines.

Drove approximately $4M in annual revenue impact by implementing Virtual Bundles to solve for inventory, sales and return issues. This enabled faster push of high-volume bundled items to market thus increasing conversion rates and repeat purchases.

Improved barcode data integrity through checksum validation and anomaly detection using Python, Hive, and PySpark reducing revenue leakage by 10–15% across large-scale item data pipelines.

Designed and implemented K-Means clustering techniques to automate item grouping on Target.com using Python and Scikit-learn reducing manual effort by 25%.

Built high-throughput data quality pipelines using SQL and Internal tools to enforce consistency, accuracy, and timeliness across large-scale item data systems, supporting reliable downstream processing.

Built Item Quality Index reporting and automated pipelines, improving data accuracy by 23% and reducing recurring data issues.

Led design and implementation of a Spring Boot + Postgres system to automate item cluster rollout, replacing legacy Elasticsearch and eliminating key bottlenecks to improve system scalability and performance.

Implemented production monitoring and alerting using Grafana, Kibana, and Slack bots, improving system reliability and ensuring high-availability.

Worked as a data engineer; extracted data using Facebook Ads API to be inserted into a cloud warehouse using Snowflake.

Worked on AWS services such as S3, Lambda, and Athena.

Worked as a data analyst centered around scores, trends, and analytics for data-driven feedback.