• Designed and implemented a three-layer pipeline with an event-driven engine for initial processing and a YAML-driven engine for subsequent stages, establishing hourly refreshes and cutting execution times by 80%• Overhauled the risk team's data pipeline, integrating advanced CI/CD practices, conducting rigorous unit testing, and implementing containerization, resulting in a 90% decrease in pipeline errors.• Facilitated the transition of data engineering workflows to Airflow, configured the platform on AWS, and created custom DAGs, enhanced pipeline execution speed, and reliability, achieving a 20% improvement in performance• Collaborated with the DevOps team to enhance AWS service utilization in data pipelines, leveraging Glue, S3, Redshift, and CloudWatch, which resulted in a 15% reduction in costs• Organized project workflow with agile methodology, conducted stand-up, sprint planning, and retrospective meetings, as well as kept documentation in Jira, Confluence & Google Drive accurate and up-to-date

• Developed a Spark-based ETL pipeline to transfer over 40 million rows of data from a PostgreSQL database to Snowflake for the company's new product, and designed Power BI dashboards, directly contributing to a 98% customer satisfaction rate• Delivered over 30 complex SQL reports, enhancing data structure efficiency and query performance for finance, compliance, and product teams, significantly improved cross-functional support, and facilitated informed strategic decision-making• Created Tableau dashboards for key business KPIs, providing in-depth and customized analysis of the company’s flagship product revenue performance, contributing to a 20% annual revenue growth for the product

• Managed Catapult POS data system and Access database, analyzed products, customer and marketing campaign data, and provided data-driven solutions to the Marketing team to recommend and support decision-making • Liaised with Marketing/Business/IT to identify business requirements, developed pricing and project plans, designed test cases, and facilitated meetings, ensuring high-quality timely implementation of data projects • Enhanced ordering and inventory systems by refining data analysis processes using Python and SQL, identifying key data typologies, and resolving data integrity issues, increasing operational efficiency by 20%

Project 1: Promotion strategy effect analysis•Implemented data preparation for throughput data, including data exploration, data cleaning, and data manipulation by using Excel Pivot Table, V-lookups, and various Excel functions such as Sum, Subtotal•Integrated weekly throughput data into two groups (promotion weekly data, regular weekly data), calculated the promotion effect for every product by Excel•Identified Top 20 products that have the most significant promotion effect by V-lookups, discovered in and out products that are always in promotion price in the Top 20 products•Generated and presented a visual report about the analytical results and recommendations to optimize promotion strategy Project 2: Warehouse management optimization•Successfully recognized top-selling products and unpopular products by conducting ABC analysis with Pivot Table•Calculated days on hand and pallet for every product, used IF function in Excel to identify products that days on hand large than 14 and pallet large than 5•Presented and interpreted analysis report to the manager using Excel and PowerPoint, provided insights and recommendations to warehouse management strategiesProject 3: Inventory management analysis•Segmented sales data from six grocery stores into three categories (Juice, Soymilk, Milk) using Excel functions, such as If, Iserror, Match, and separated six grocery stores into two groups: shop 229 (new inventory management) and other five shops (old inventory management)•Performed sales and profit research by calculating sales and profit difference between shop 229 and the other five shops, provided presentations via Excel charts to the manager•Discovered the profit of shop 229 less than the other five shops in Soymilk and Milk categories, analyzed the cause of this result by using Excel Pivot Table and V-lookups•Presented visual report about the analytical results to the manager to optimize inventory management

•Conducted directional statistics research, studied the statistical properties of circular observations which are frequently observed in many scientific disciplines, such as Biology, Sports, Image analysis•Studied several statistical models (e.g., von Mises distribution), visualized these models by using Python lib numpy, matplotlib, and many R packages, such as rotations, circular, wrapped•Collected two circular data sets from an online database, presented plots for these two data sets by using R package circular•Performed data and quantitative analysis using R on wind direction data to deliver insights on the seasonal variation of the wind direction. Used the EM algorithms in R to build mixed Von mises models to analyze turtle moving direction data, successfully identified the moving direction for two group turtles•Generated detailed and visual report, presented theoretical basis and practical application about the circular observations to my professor, and my professor thought highly of my work