San Francisco Bay Area
Senior Analytics & Data Engineer with 7+ years of experience at Amazon and Apple, specializing in large-scale data engineering, advanced data modeling, A/B testing, and AI-driven automation. Proven success in architecting scalable ETL pipelines, deploying LLM-powered analytical agents, and converting billions of complex supply chain and product records into strategic business initiatives. Adept at cross-functional program management, bridging the gap between deep technical execution and executive-level product roadmap strategy.
▪ [AI & Data Engineering] Architected and deployed an LLM-powered multi-agent analytical system (SSD-Sage) using Python and AWS Bedrock/RAG; translated natural-language business queries into optimized distributed SQL, automating data incident root-cause analysis and slashing manual engineering investigation time by 85%. ▪ [Supply-Demand & Data Modeling] Spearheaded an end-to-end global supply chain attribution and predictive forecasting model from scratch; designed robust dimensional data models to capture system latency and operational defects, boosting global in-stock rates by 10%. ▪ [Experimentation & Analytics] Designed and executed analytical frameworks to evaluate new supply chain features; defined business driver and system guardrail metrics (e.g., system latency, tracking errors) to ensure data integrity and statistically sound deployment. ▪ [Program Management & Scaling] Developed an All-in-One core analytics matrix processing billions of records across Redshift and S3; automated 90% of recurring business reviews (WBR/MBR) and built cross-team Tableau/QuickSight dashboards providing SKU-level demand visibility to 100+ global stakeholders. ▪ [Leadership] Partnered closely with Director-level leadership to align global inventory strategy with product goals; spearheaded data governance initiatives and mentored junior engineers through code reviews and architecture design guidance.
▪ [Hardware Compliance & Operations] Coordinated with cross-functional Worldwide Supply-Demand, Regulatory, and Product Analysis teams to drive data strategy for Product Lifecycle Management (PLM); synthesized manufacturing and Bill of Materials (BOM) data to mitigate supply risks during early prototyping. ▪ [Relational Modeling & DB Design] Designed and scaled production relational models and SQL data schemas linking complex multi-tier supplier, part, and material dimensions within PLM systems, guaranteeing 100% data integrity across global hardware lifecycle tiers. ▪ [Data Pipeline Automation] Engineered automated Python data pipelines to ingest, clean, and monitor restricted substance telemetry from thousands of global third-party suppliers; slashed compliance reporting cycle times by 90% and accelerated data incident response. ▪ [Visual Analytics & ROI] Created enterprise-wide interactive analytics platforms, transforming raw supplier metrics into actionable compliance strategies that reduced global hardware testing costs by 20%.
▪ Collaborated with cross-functional teams, monitoring deadlines and quality control initiatives. ▪ Saving 20% budget when finished the project and ahead of schedule. ▪ Managing risks and issues of projects. ▪ Assist the company to get the government project “Taipei City Soil and Groundwater Pollution Investigation and Verification Program 2017-2018”. ▪ Private site pollution remediation work and analysis of groundwater pollution flow direction
▪ Combined environmental knowledge and multivariate statistical analysis skills to complete groundwater pollution source tracking and analysis. ▪ Utilizing cluster analysis for the grouping based on contamination characteristics of groundwater quality and applying PCA to identify major principal components. ▪ Applying SPSS and Minitab to analyze groundwater quality.