• Utilized AWS S3 to ingest and manage over 200,000 financial transaction records, creating a scalable and secure storage layer that supported reliable downstream anomaly analysis, data access for finance teams, and streamlined integration with automated processing systems.

• Developed ingestion workflows using AWS Lambda, reducing manual file handling efforts by 40%, while enabling event-driven data processing that improved responsiveness and reduced data lag for timely detection of unusual patterns in high-volume financial logs.

• Processed 320 GB of structured financial data in AWS Databricks, applying distributed transformations to enhance anomaly coverage by 18%, while improving collaboration by building and documenting all workflows in shared notebooks accessible to data and finance stakeholders.

• Leveraged Alteryx to clean, merge, and transform 85,000+ loan applications, ensuring consistent formatting and data validation rules across datasets, which enabled accurate anomaly flagging and significantly reduced data quality issues during analysis cycles.

• Created SQL pipelines in Snowflake to integrate 5 data sources across departments, improving refresh times by 20%, allowing the anomaly scoring engine to run on current financial inputs with greater reliability and reduced query overhead.

• Built financial dashboards using Power BI to visualize anomalies greater than $10K, giving finance leadership instant visibility into variances and enabling proactive investigation of outliers before they impacted monthly reporting cycles or audit checkpoints.

• Applied anomaly detection logic in Python to model suspicious trends in 250,000+ payment records, reducing late detection of payment irregularities by 15%, and providing early alerts that supported faster corrective actions from operations teams.

• Wrote optimized SQL queries in Redshift to track 7 core financial KPIs, improving report performance by 25%, and enabling more frequent variance analysis with less strain on compute resources and data engineers.

• Used Excel VBA Macros to automate GL validation checks, flagging ±3% anomalies across 12 cost centers and saving over 12 hours/month of manual reconciliation for junior analysts on the accounting team.

• Maintained version control of scripts and models in GitHub, tracking iterative enhancements across 2 key anomaly detection workflows, ensuring reproducibility, collaboration, and a structured history of changes for audit and compliance review.

Tools: AWS S3, AWS Lambda, AWS Databricks, Snowflake, Amazon Redshift, SQL, Python, Alteryx, Microsoft Power BI, Anomaly Detection, Distributed Data Processing, Financial Data Analysis, Data Validation & Quality Checks, Excel VBA Macros, GitHub, Version Control, Event-Driven Pipelines, Data Visualization, Audit & Compliance Reporting

• Designed and developed a comprehensive real-time supply chain visibility dashboard using Power BI, integrating data from multiple sources, including logistics, warehouse management, and manufacturing systems, to provide stakeholders with a unified, interactive view of the supply chain.

• Implemented advanced data integration solutions leveraging AWS services such as Amazon S3 and AWS Glue to efficiently extract, transform, and load (ETL) large volumes of supply chain data from disparate systems, achieving a 40% improvement in data processing speed and accuracy.

• Engineered custom data pipelines and optimized SQL databases to ensure seamless data flow and real-time updates, enabling accurate tracking of inventory levels, order statuses, and shipment progress, while reducing data retrieval times by 30%.

• Applied statistical and machine learning techniques to forecast potential bottlenecks and delays in the supply chain, resulting in the proactive identification of issues and a 15% improvement in overall operational efficiency.

• Developed automated reporting mechanisms using Excel macros and VBA scripts, streamlining data extraction, and reporting processes, and reducing manual errors by 25%, ensuring stakeholders received accurate, timely supply chain insights.

• Enhanced dashboard functionality by incorporating predictive analytics for inventory and demand forecasting, reducing stockouts and excess inventory by 10%, and improving the overall cost-efficiency of the supply chain.

• Promoted cross-functional collaboration in an Agile environment, actively contributing to sprint cycles and continuously iterating on the dashboard based on feedback from key stakeholders, leading to improved supply chain performance.

Tools: Amazon S3, AWS Glue, Extract, Transform, Load (ETL), SQL databases, Microsoft Power BI, Machine Learning, Statistical Data Analysis, Excel macros, VBA scripts, Predictive Analytics, Agile Environment.