Data Analyst and Computer & Information Science graduate at BYU-Hawaii with experience across data analytics, machine learning, cloud data engineering, predictive modeling, and enterprise data governance. Developed scalable analytics and ML solutions using Python, SQL, Snowflake, AWS, and Power BI to support organizational decision-making, automation, and operational reporting.
Built end-to-end data workflows involving extraction, transformation, preprocessing, modeling, visualization, and deployment across enterprise and cloud-based environments. Experienced developing ETL pipelines, integrating large datasets, optimizing cloud data storage, and automating analytics processes using AWS services including SageMaker, Glue, Athena, Redshift, Lambda, Kinesis, EMR, and S3.
Designed and evaluated supervised machine learning models using Amazon SageMaker and Scikit-learn, including workflows for feature engineering, hyperparameter tuning, model validation, and real-time inference deployment. Applied predictive analytics techniques to forecasting and classification problems using structured datasets and statistical evaluation metrics.
Developed executive-facing dashboards and reporting systems in Power BI, Tableau, and DOMO, translating complex datasets into actionable insights for institutional leadership and cross-functional stakeholders. Conducted predictive enrollment forecasting and enterprise reporting initiatives supporting strategic planning and operational decision-making.
Led data governance initiatives establishing organization-wide standards for Power BI development, reporting consistency, data access practices, and analytics documentation across multiple departments. Supported analytics team development through technical training, collaboration, and process standardization.
Technical foundation in:
Data Analytics
Machine Learning
Cloud Data Engineering
Business Intelligence
Predictive Modeling
ETL Pipeline Development
Data Governance
Enterprise Reporting
AWS Analytics Architecture
Technologies include Python, SQL, AWS, Snowflake, Power BI, pandas, Scikit-learn, NumPy, Apache Hive, and distributed cloud-based analytics workflows.
