San Francisco Bay Area
Payment Systems Data Analyst | Building Analytics That Drive Business Decisions I specialize in transforming financial and payment data into actionable insights. At PayPal, I've optimized reporting infrastructure, built predictive models, and created dashboards that directly impact business decisions across enterprise operations. WHAT I'VE DELIVERED: - Optimized Snowflake queries processing 14M+ financial records → 24% faster reporting - Built predictive risk models (Scikit-learn) for 3.6M+ customers → 21% improved forecasting accuracy - Implemented Tableau dashboards tracking revenue leakage and portfolio performance metrics - Reduced query latency from 4+ hours to 20 minutes → enabled real-time stakeholder decisions - Automated Python/Pandas ETL workflows in AWS → eliminated manual data validation bottlenecks TECHNICAL SKILLS: Database: Snowflake, PostgreSQL, MySQL, Oracle, SQL Server Programming: Python (Pandas, NumPy, Scikit-learn, TensorFlow), Scala, R. Visualization: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP) Cloud: AWS (S3, EC2), Azure, dbt (data transformation) Methods: Predictive Modeling, Time Series Analysis, A/B Testing, Root Cause Analysis, ETL/ELT Processes MY APPROACH: I don't just create dashboards—I solve problems. I focus on understanding what decision-makers actually need, then delivering analytics that save time, reduce risk, and drive revenue. CURIOUS ABOUT: - Payment systems, risk analytics, and FinTech infrastructure - ML/AI applications in financial services - Building scalable, maintainable data platforms OPEN TO: Data Analyst, Analytics Engineer, and BI Developer roles in FinTech, Payment Systems, and SaaS companies. If you're building financial intelligence, let's talk. 📧 [email protected] | 📱 (551) 235-9086
ROLE IMPACT: Analyzed financial transaction data and customer-risk datasets to support enterprise analytics operations, optimizing reporting infrastructure and building predictive models for 14M+ financial records. KEY ACCOMPLISHMENTS: 📊 REPORTING OPTIMIZATION - Optimized Snowflake warehouse queries and table structures processing 14M+ financial records → 24% improvement in reporting performance - Reduced dashboard query latency from 4+ hours to 20 minutes - Enabled real-time stakeholder access to revenue leakage metrics and portfolio performance trends 🤖 PREDICTIVE MODELING - Built and deployed Scikit-learn risk stratification models across 3.6M+ customer records - Improved financial utilization forecasting accuracy by 21% - Enhanced demand planning and resource allocation efficiency across enterprise operations 📈 DASHBOARDING & INSIGHTS - Designed Tableau dashboards tracking revenue leakage, portfolio performance metrics, customer transaction trends, and KPI analysis - Created executive-level reporting supporting financial operations optimization initiatives - Identified abnormal transaction patterns using hypothesis testing and time series analysis 🔄 DATA ENGINEERING - Automated Python and Pandas preprocessing workflows within AWS environments - Streamlined transaction validation and customer verification checks - Developed dbt transformation models using Git/Bitbucket within Agile delivery - Improved financial reporting consistency and enterprise data warehousing efficiency TECHNICAL STACK USED: Snowflake, SQL, Python (Pandas, NumPy, Scikit-learn), Tableau, AWS (S3, EC2), dbt, Git, Bitbucket, Agile/SCRUM methodologies
Analyzed customer transactions, fulfillment operations, and retail datasets supporting 50+ analytics projects. Optimized reporting infrastructure for e-commerce operations processing 9M+ order records monthly. KEY ACCOMPLISHMENTS: 📊 QUERY OPTIMIZATION & PERFORMANCE - Optimized SQL and MySQL reporting queries processing 9M+ order records - Improved retail dashboard performance by 19% → accelerated fulfillment reporting for operational stakeholders - Reduced query execution time from 2+ hours to 12 minutes, enabling real-time inventory visibility 🏪 RETAIL ANALYTICS & KPIs - Developed Power BI dashboards tracking cart-conversion metrics, fulfillment efficiency, inventory movement, and logistics coordination performance - Monitored fulfillment operations across 3.1M+ logistics events - Conducted root cause analysis that reduced shipment-delay escalations by 18% 👥 CUSTOMER INSIGHTS - Evaluated customer purchasing trends using Matplotlib and Seaborn visualizations - Identified seasonal demand patterns → improved promotional campaign engagement rates by 16% across multiple product categories - Supported customer-retention reporting initiatives 🔄 DATA ENGINEERING & PIPELINES - Developed SSIS ETL pipelines and collaborative reporting workflows - Streamlined Python and NumPy workflows within Azure environments - Standardized customer-order validation and transaction cleansing processes - Improved retail data integration consistency across SDLC-driven analytics delivery - Supported cross-functional business teams with reliable data infrastructure 📋 OPERATIONAL ANALYSIS - Assessed retail sales and inventory datasets using Advanced Excel functions - Supported order reconciliation and fulfillment performance reporting activities - Enabled inventory optimization and shipment visibility initiatives TECHNICAL STACK USED: SQL, MySQL, Python (NumPy, Pandas, Matplotlib, Seaborn), Azure, Power BI, Advanced Excel, SSIS, GitHub, SDLC methodologies