Developed and maintained SQL queries and Python scripts to extract, transform, and load (ETL) data from disparate sources, reducing manual data preparation time by 25%.

Created interactive dashboards using Tableau and Power BI, visualizing key performance indicators (KPIs) that enabled stakeholders to monitor progress and identify opportunities, leading to a 15% improvement in reporting efficiency.

Performed statistical analysis on customer behavior data, identifying segments with high churn risk and contributing to a targeted retention strategy that reduced customer attrition by 10%.

Collaborated with product and marketing teams to define data requirements and implement tracking mechanisms for new features, improving data accuracy by 20% for campaign performance metrics.

Automated weekly reporting processes using Python and Excel, saving approximately 8 hours per week for the business operations team and ensuring timely access to critical business insights.

Assisted in cleaning and validating large datasets using SQL and Excel, ensuring data integrity for critical business reports and reducing data error rates by 18%.

Generated ad-hoc reports and presentations for internal stakeholders, providing data-backed answers to specific business questions and facilitating informed discussions.

Contributed to the development of a new data dictionary, standardizing definitions for key metrics across the organization and improving data literacy among non-technical teams.

Participated in A/B testing analysis for website optimizations, helping to identify changes that increased user engagement by 7% on key landing pages.

Learned and applied basic machine learning concepts to assist in predictive modeling projects, enhancing understanding of future trends and potential business impacts.