Seattle, Washington, United States
I am a Data & Analytics professional with 12+ years of experience spanning Data Science, Analytics Engineering, Business Intelligence, and Data Product Management. I help organizations build trusted data foundations, scalable data products, and AI-powered decision systems that transform data into measurable business outcomes. My experience spans building data products, leading high-performing teams, designing analytics platforms, developing semantic layers, driving experimentation, and enabling self-service analytics at scale. I partner closely with Product, Engineering, Finance, Marketing, Operations, and executive leadership teams to translate complex business challenges into actionable insights, strategic roadmaps, and data-driven decisions. While I have extensive experience building and coaching analytics teams, I remain deeply hands-on across SQL, Python, dbt, data modeling, cloud data platforms, BI technologies, and modern data stack. More recently, I have focused on leveraging Generative AI, Agentic AI, Claude Code, Codex, MCPs, and LLM-powered workflows to accelerate analytics delivery, automate insight generation, improve productivity, and create more intelligent decision-support systems. I am particularly passionate about operating at the intersection of data, AI, and business strategy, building systems that not only explain what happened, but help organizations understand why it happened, predict what is likely to happen next, and recommend the best actions to take. I am particularly interested in leadership and senior IC-level roles across Data Science, Analytics Engineering, Data Products, Business Intelligence, Product Analytics, and AI-powered analytics, where I can help organizations build intelligent data ecosystems that drive measurable business impact.
Focused on Team Leadership, analytics engineering, product data science, Business Intelligence and Agentic AI-enabled analytics initiatives across large-scale data environments. Core areas: Product Data Science · Analytics Engineering · Agentic AI · Business Intelligence · dbt · SQL · Python · AWS · Claude Code · Data Modeling · A/B Testing · Tableau · Team Leadership · Stakeholder Communication
At Amazon, I led and developed a high-performing analytics team while delivering scalable analytics solutions that informed strategic product and business decisions. I partnered closely with cross-functional stakeholders to define KPIs, design measurement frameworks, and lead A/B experimentation to evaluate feature performance and user engagement. In parallel, I remained hands-on across SQL, data modeling, and analytics engineering, building efficient and scalable data foundations that improved decision-making and enabled self-serve analytics at scale.
Promoted to L6 and operated as a Lead contributor in product data science, building scalable data models and ELT pipelines on Redshift to support large-scale analytics and reporting. Applied advanced SQL (CTEs, window functions, complex joins) to analyze large datasets and deliver deep-dive insights for leadership. Designed KPI frameworks and self-serve dashboards using Tableau and QuickSight, significantly improving data accessibility and reducing decision cycles. Led the design and analysis of A/B experiments and built reporting infrastructure to measure feature performance, driving improvements in adoption and engagement. Partnered with product teams to understand user behavior, define success metrics, and evaluate product initiatives, while ensuring data reliability through quality checks and standardized data definitions.
Built foundational data models and pipelines on Redshift to support product and business analytics, enabling consistent and reliable reporting across teams. Played a key role in the Learning Genome initiative, a data-driven framework for modeling user learning behavior and content relationships to support personalization and recommendation strategies, where I analyzed user engagement patterns and contributed to scalable data structures powering these use cases. Developed dashboards and reporting solutions to track key performance metrics and provide visibility into business trends. Performed exploratory data analysis to identify growth opportunities and support data-driven decision-making. Collaborated with cross-functional stakeholders to gather requirements and deliver scalable analytics solutions, while optimizing SQL queries and data processes to improve performance and efficiency.
At Florida Atlantic University, I applied data science and statistical analysis principles to improve student success, retention, and enrollment outcomes. I worked across student lifecycle data to define key metrics, conduct cohort and longitudinal analyses, and build predictive models to identify at-risk students and inform early interventions. I partnered with academic and administrative stakeholders to translate ambiguous problems into structured analyses and actionable insights, enabling data-driven decision-making and more effective resource planning. Tech stack: Python (pandas, scikit-learn), SQL, Tableau/Power BI, Excel, statistical modeling, A/B testing, data modeling.
Transformed Manual Reporting to Automated BI for 10,000+ Customers. Led BI strategy and implementation for enterprise clients, delivering automation and insights at scale. • Automated Reporting at Scale: Achieved 99% reduction in manual reporting time from 12 hours to seconds—by automating 50+ reports with Tableau, enabling real-time insights for 10,000+ customers • Developed BI Strategy: Created and implemented comprehensive BI roadmap that aligned technology capabilities with business objectives, establishing foundation for data-driven decision-making • Built Enterprise Dashboards: Designed and deployed Tableau dashboards that became the primary interface for business monitoring and strategic planning across client organizations • Enabled Self-Service Analytics: Empowered business users to generate their own insights by creating intuitive, governed reporting tools Key Achievement: Transformed client’s data culture from reactive reporting to proactive analytics, demonstrating clear ROI within first quarter Tech Stack: Tableau, SQL, BI Strategy, Data Warehousing