• Analyzed large datasets using Python and SQL, reducing data processing time by 30% through optimization of ETL pipelines.• Created interactive dashboards in Tableau and Power BI, improving data visibility and decision-making across departments by 40%.• Designed predictive models using R to forecast sales, increasing forecast accuracy by 20% and saving $250K annually in inventory costs.• Automated reporting processes with Python, saving 10 hours weekly for the finance team.• Established and maintained a centralized database in Snowflake, enhancing data accessibility and reducing redundancy by 50%.• Led a cross-functional team to integrate GCP BigQuery for advanced analytics, improving query performance by 35%.• Collaborated with stakeholders to deliver actionable insights that drove a 15% increase in marketing campaign ROI.

• Conducted exploratory data analysis on customer behavior, leading to a 12% increase in user retention rates.• Built KPI dashboards for the operations team, reducing reporting time by 40% and improving operational efficiency.• Cleaned and validated datasets of over 2M records using Python and Excel, ensuring 99.9% data accuracy.• Supported migration of legacy databases to Azure, reducing downtime by 25% during implementation.• Developed SQL queries to streamline financial reporting, cutting monthly processing time by 15%.

• Conducted market research and analyzed survey data, identifying key trends that increased product adoption by 8%.• Assisted in building machine learning models to predict churn rates, reducing customer attrition by 5%.• Created weekly dashboards in Tableau to track business performance, improving reporting efficiency by 30%.• Extracted and cleaned data from multiple sources using Python and SQL, ensuring consistency for analysis.• Automated data collection processes, reducing manual effort by 20 hours per month.