• Developed observability solution using Aurora database for merchant reporting delivery tracking supporting millions of merchants

• Developed email notification for 400+ merchants to alert on report delivery delays due to data issues

• Developed email notification system to alert reporting team for any validation failures internally

• Optimized EMR performance to support report generation for top merchnats to scale for terabytes of data

• Designing application to visualize key business and operational metrics to monitor data system health

• Optimized Spark performance 2x for terabyte-scale ingestion from ad server, saving $20,000+ each

quarter

• Led a team to deploy data pipelines in Airflow for a deep learning model to assess campaign effectiveness

• Guided engineers in identifying and resolving data quality issues during measurement processes

• Productionized ETL pipelines and classification model for automating ad video mapping, saving 150 work hours each quarter

• Guided engineers in identifying and resolving data quality issues during measurement processes

• Streamlined process for internal and external data ingestion and validation of Alexa advertisement data

• Worked on community detection in social networks under the supervision of Prof. Marianne Winslet

• Investigating techniques to visualize clusters in knowledge graphs

• Supervising undergrad students to enable development of identified techniques for community detection

• Analyzing fact-prediction algorithms in knowledge graphs

• Developed an application in Pytorch to predict new facts in knowledge graph

• Paper published at CIKM 2020, https://dl.acm.org/doi/10.1145/3340531.3412023

• Paper published at WWW'21: https://dl.acm.org/doi/10.1145/3442381.3449856