Data Analytics Engineer
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Graduated Bachelor of Science in Data Theory at UCLA with an academic background in statistics, data, and ML. Experienced in data warehousing and ETL. Proficient in leveraging all types of data to drive analytical insights in any field.
Data Analytics Engineer with hands-on experience in healthcare, research, and enterprise data systems. At the Center for Family Health and Education, designed and optimized end-to-end ETL/ELT pipelines, automated data validation and governance, and developed scalable data architectures that improved compliance reporting and boosted clinical visibility by 200%. At UCLA, conducted advanced statistical modeling and clustering analyses to uncover environmental and health-related insights, accelerating study timelines by 84%.
At Intel, contributed to Agile-driven automation initiatives by developing a configurable script to clean and maintain critical directories, strengthening data reliability and workflow efficiency. Proven ability to lead cross-functional data teams, enhance data quality, and translate complex systems into actionable business insights
Completed a Bachelor of Science in Data Theory at UCLA with a comprehensive foundation in data science—covering data management, statistical modeling, classical and deep machine learning, and the mathematical principles behind AI/ML. Gained hands-on experience applying ethical, scalable, and tool-driven approaches to real-world data problems using modern frameworks and best practices.
