• Designed and implemented data models within Tableau and Power BI to support audit testing for mortgage lending, capital markets, corporate finance, information technology, and credit risk reporting.• Developed SQL and Python scripts to batch data into Internal Audit’s data mart. Data contained on the data mart was used to support 7 Power BI dashboards and 2 Tableau dashboards.• Created SQL scripts to reconcile loan amounts to the general ledger with a 1% margin of error. As a result, Regions was able to verify the completeness and accuracy of data contained on the data lake.• Ensured personally identifiable information (PII) was encrypted, thereby guaranteeing compliance with internal policies and regulatory standards.

• Evaluated the design and effectiveness of internal controls over the following areas: authentication and security infrastructure, business resilience, deposit operations, online banking transactions, and customer risk.• Validated e-signature integration for remote document signing with a focus on encryption, multi-factor authentication, and comprehensive testing.• Developed SQL queries regarding mortgage and home equity loans. The queries ensured that borrowers were screened for applicable regulations and internal policies. • Created a Power BI dashboard to analyze adoption metrics of internal reporting dashboards. This solution provided Internal Audit with valuable insights regarding user engagement and dashboard usage across the department.

• Implemented a MongoDB database to store payroll expense data for 7,000 employees. This database ensured compliance with state and federal laws requiring the disclosure of public university payroll expenses.• Corresponded with the University's senior web architect to discuss the requirements, security, and execution of the project. This ensured that project deliverables were completed with respect to the initial project timeline.• Programmed an ETL script that imported data from .XML files into the database. This ETL script was written in Python and allowed for the user to upload data locally or to Microsoft Azure.

• Drafted reports pertaining to customer demographics, marketing analytics, and product sales. The reports provided insights regarding how to acquire new customers.• Built an automated lead generator in Python that pulled customer data for a county from Yellow Pages. This program provided the sales team with 300 new leads.• Established a customer database in SQL Server. Performing queries on this database provided Tango Tango with the following information: sales by product type, stock of product, customers by geographic location, and customers by industry.