● Developed complex SQL queries on Snowflake to extract transactional data, providing insights into customer spending patterns.

● Built and maintained critical data dashboards in Looker, enabling teams to monitor corporate card usage and expense categories effectively.

● Utilized Python with Pandas to clean and transform raw financial data, ensuring accuracy for compliance and reporting requirements.

● Designed and automated data validation checks using SQL, significantly improving data quality for all financial and operational metrics.

● Generated comprehensive reports using Tableau, detailing expense management trends and identifying opportunities for cost optimization.

● Created data models in dbt to standardize key financial metrics, providing a consistent view across various analytical projects.

● Managed data ingestion workflows using Airflow, ensuring timely and reliable delivery of data for daily business operations.

● Performed ad-hoc analysis using Python, identifying discrepancies in transaction data to support fraud detection efforts.

● Developed predictive models in R to forecast future spending behaviors, assisting in proactive financial planning and risk assessment.

● Built and iterated on A/B testing frameworks in SQL, evaluating the impact of new features on user engagement and retention.

● Used SQL to extract and aggregate financial transaction data from large relational databases, supporting monthly reporting requirements.

● Developed interactive dashboards in Tableau for tracking key banking product performance and customer engagement metrics.

● Performed data cleaning and transformation using Python with Pandas to prepare raw customer data for analytical modeling.

● Conducted ad-hoc analyses using SQL and Excel to investigate anomalies in customer account activities and identify root causes.

● Built reports in Power BI visualizing marketing campaign effectiveness and customer acquisition channels for retail banking products.

● Prepared data quality reports using SAS to ensure compliance with financial industry regulatory standards for data accuracy.

● Created detailed documentation for data definitions, sources, and transformation logic, improving data governance and accessibility.

● Analyzed transaction datasets using Python to identify potential fraud patterns, contributing to enhanced security measures.

● Developed customer segmentation models using R for targeted marketing strategies, improving personalization of financial product recommendations.

● Supported data pipeline operations by validating extracted data for accuracy and completeness, using SQL queries on Snowflake.