Talent Profile

Data analyst with 2+ years of experience in Python, SQL, BigQuery, Tableau, and Grafana. Skilled in ETL, data cleaning, modeling, visualization, and pipeline monitoring. Key achievements include improving reporting accuracy by 25% through data cleaning and deduplication, reducing ad campaign costs by 15% via SQL-driven optimization, and identifying drugs with up to 75% deviation in adverse event spikes for early safety intervention.

• Supervised standardized testing for 1500+ students across multiple school sites, coordinating with team of 30+ examiners • Cross-validated test scores and records across multiple Excel sheets, ensuring 100% data accuracy and confidentiality • Validated inventory of 1000+ testing materials, flagging missing or miscounted items to improve resource availability • Prepared test rosters, tracked attendance, and organized test incidents, improving data accuracy by 15% and streamlining rescheduling • Supervised standardized testing for 1500+ students across multiple school sites, coordinating with team of 30+ examiners • Cross-validated test scores and records across multiple Excel sheets, ensuring 100% data accuracy and confidentiality • Validated inventory of 1000+ testing materials, flagging missing or miscounted items to improve resource availability • Prepared test rosters, tracked attendance, and organized test incidents, improving data accuracy by 15% and streamlining rescheduling • Analyzed 575K+ FDA adverse event records in BigQuery and SQL, identifying drugs to support early detection of safety risks • Detected abnormal spikes in events via SQL, flagged 2 drugs with up to 75% deviation for early safety risk intervention • Built Grafana dashboard to track monthly adverse event volumes and severity, enabling early identification of risky drugs • Cleaned and validated 575K+ adverse event records using SQL, confirming 100% data completeness for reliable insights • Prepared and validated 100GB+ OpenFDA data, enabling up-to-date datasets for downstream analysis and reporting • Cleaned and deduplicated patient, drug, and reaction data using Python, improving accuracy and reporting time by 25% • Created Grafana dashboards with real-time alerts for pipeline health, identified transformation bottleneck and cutting average processing time from 7 to 2 hours • Monitored data quality (null values and ratios) via dashboards, alerting data engineers for timely issue resolution and ensuring downstream analysis reliability

M.S, Computer Science @ Illinois Institute of Technology