Solution-driven Business Intelligence Engineer with 3 years of experience delivering core data products by developing scalable data models and ETL pipelines. Proven track record of collaborating with stakeholders to build data solutions and produce impactful analytics.
Develop scalable data models and ETL pipelines, enabling transparent and automated reporting of core metrics for a $550M book of business.
• Collaborate with Finance and Customer Experience leadership to automate all reporting of all account management metrics in Snowflake and Tableau, reducing manual reporting time by 100 hours monthly.
• Partner with Salesforce Architects to automate ETL pipeline for analyzing and identifying flawed closed deals, resulting in a 17% reduction in Revenue Operations support tickets.
• Create Python-based analytical model to extract, score, and load entities from the PitchBook platform to Salesforce, resulting in a 7% increase in demo completion rates.
• Construct Salesforce ELT pipelines using Python, Airflow, and DBT, increasing data refresh frequency by 300%.
• Implemented slow-changing dimensional modeling of key business metrics to enhance data governance and support comparative analytics solutions.
• Collaborate with software engineers to improve newsletter data accuracy by developing a Python data transformation pipeline to analyze subscriber usage and eliminate suspicious user behavior from reporting.
Developed and optimized ETL pipelines and quality assurance functions to enhance forecasting and dashboard accuracy.
• Automated 14 ETL pipelines in Airflow to transform, test, and blend diverse data sources, leading to a 40% enhancement in the accuracy of the global vaccine supply model.
• Collaborated with data scientists to identify modeling bottlenecks and implemented custom data warehouse tasks, resulting in a 6-hour monthly reduction in manual data entry time.
• Utilized Python to blend data from public and confidential sources, creating a Monte-Carlo simulation for precise estimation and forecasting of vaccine demand in private markets.
• Developed Python scripts to identify discrepancies across data sources, increasing supply forecast accuracy by 15%.
• Engineered backend database design of the UNICEF Vaccine Market Dashboard, resulting in improved data accuracy and a reduction in downtime by 87% monthly.
• Developed ETL pipelines in R to transform, blend, and automate the ingestion of 150 MICS datasets, leading to a 45% increase in the model sample size when integrated with DHS surveys.
• Created impactful data visualizations, effectively presenting statistical and technical information in a clear and concise manner, contributing to transparent understanding of research findings.