Data Analyst
• Led Fraud Detection System Project: Developed a system using MySQL, Python, and Tableau to analyse 10M+ e-commerce transaction records, identify fraud patterns, reduce fraudulent activities by 30%.• Designed and Implemented Database Schema: Created an optimized schema in MySQL and SQL Server to store and query large datasets (10M+ records) efficiently, ensuring data integrity and improving query performance by 40%.• Enhanced Data Visualization: Built interactive dashboards in Tableau Desktop and Microsoft Power BI for real-time monitoring and reporting, reducing fraud detection response time by 25%.• Collaborated with Cross-Functional Teams: Worked with 5+ engineering and operations teams to integrate fraud detection algorithms into the existing e-commerce transaction processing system, enhancing collaboration and communication.• Conducted Data Wrangling and Cleaning: Utilized R packages (dplyr, tidyr) and Python libraries (Pandas, NumPy) to clean and prepare data, improving data quality by 20% and reducing data processing time by 15%.• Performed Statistical Analysis: Used R and Python for statistical analysis and modelling, including time series analysis with forecast and tseries packages, enhancing prediction accuracy by 25%.• Utilized ETL Tools: Employed Tableau Prep and Talend for ETL processes, enhancing data integration efficiency by 25%.• Enhanced Data Workflow Automation with Alteryx: Utilized Alteryx to streamline and automate complex data workflows, reducing manual data processing by 30% and improving data preparation for real-time reporting.• Orchestrated ETL Pipelines Using Apache Airflow: Implemented Apache Airflow to manage and automate ETL workflows, ensuring smooth and timely data ingestion from MySQL to AWS Redshift, reducing data latency by 25%.• Integrated CI/CD Processes: Automated deployment of data pipelines using Jenkins, reducing deployment time by 40% and ensuring consistent updates across platforms.