Participated in Business requirement sessions and effectively communicated to data and visualization engineers on the team.

Performed detailed Data Analysis, Data Profiling, Data Cleansing, Data Quality, and Data Integrity using complex SQL/Excel for 5+ data assets discussed results with the Business and Design team, and documented the mapping rules.

 Crafted SQL queries Logic for aggregate views using Snowflake DB for 5+ data assets.

Created Data Mapping Specifications, to map data from Source to Target and service specifications for API for 3+ data assets.

Drafted and built 5+ Tableau dashboards using the extracted data, showing 20+ business metrics of 400k customers with various filters, and tested them for accuracy and user-friendliness before rolling them out.

 Utilized PySpark for efficient data transfers from the data lake to Data bricks, ensuring 99.5% accuracy across systems.

Collaborated with Prof. Vivian Huangfu on the Russian-Ukraine war project and analyzed over 500,000 tweets, leading to the design of a sentiment analysis model to understand people's sentiments on conflict.

Developed a sentiment indicator from data that matched 95% of known market trends, especially in the energy sector.

Contributed to the development of a Question Answer model under Prof. Huangfu, achieving an accuracy rate of 85% in extracting answers from more than 2000 research papers.

Assisted Professor Liang Ma and Ran Zhao in acquiring and profiling BLS economic release data. Explored over 100,000 data points and used PowerBI to visualize 10 main patterns.

 Studied Accidents in the USA using 2 million+ accident records and visualized identified patterns using Tableau.

Created a Machine Learning model to predict the win of a Base Ball Home team using the real-time baseball data stored in 10+ tables in MariaDB using Datagrip, and SQL and containerized the entire project for reproducibility using bash script and custom docker.

Designed and automated data models for Amazon Selection and Catalog Systems, processing over 5 million records daily using AWS tools (S3, Glue, Athena), Python, and Pyspark. Enhanced the existing codebase through automation, resulting in a 25% efficiency boost in managing product datasets across marketplaces.

Managed and maintained Redshift clusters, optimizing data lakes and pipelines while implementing thorough monitoring systems to ensure flawless ETL execution.

Spearheaded the development of STMs and Data dictionaries for building integration and aggregation on the data lake.

Collaborated with the AWS infrastructure for solid analytics support and orchestrated complex data pipelines for real-time analytics on product data using Airflow.

Automated 20+ Weekly and Monthly Business review dashboards to offer fresh insights to stakeholders using a stone branch scheduler.

Designed and implemented an automated workflow for data scraping and brand-based categorization, resulting in a 30% reduction in manual intervention, thereby optimizing efficiency and accuracy in the data processing pipeline.