Yuling W.
- Role
- Business Intelligence Engineer Ii at Amazon
- Location
- Seattle, WA, US
- LinkedIn followers
- 500 followers
About Yuling W.
Analytics Engineer with 6 years of experience in metrics creation, visualization and…
Experience
Business Intelligence Engineer Ii
Oct 2019 — Present
Current Team: Amazon Shipping (2023/5~) Prime Video Product Analytics (20••••21) XCM (Cross-Channel Marketing Measurement) Engineering Science (20••••23) Reporting Dashboards, Data Pipelines and Automation • Led the analytics initiatives to build marketing campaign configuration database and established data ingestion SoP as the single source of truth for reporting cross-channel Ad effectiveness without manual touch • Built data models and ETL pipelines for scalable analytical products to automate customer sampling, benchmark metrics validation and visualization using Tableau and AWS services including Redshift, S3, Glue, Athena, QuickSight • Automated survey weighting in R using Random Iterative Method (RIM) to power Economists’ Double Machine Learning (DML) models • Optimized query efficiency by creating speed layers using Spark SQL - reduced the cost of scanning large tables, accelerated job execution time and enabled the auto-resolution of error tickets Metrics Creation and A/B Testing • Owned the proxy creation, calculation and roadmap planning for non-guardrail metrics that measure customers’ content discovery intent and success on Mobile, Web and Living Room devices • Performed end-to-end A/B testing in UI change and Personalization experiments - Experiment design doc review, metrics definition, launch and dial-up criteria selection, statistical analysis on metric sensitivity, and results presentation to PMs and senior leadership on global rollout decision • Improved clickstream data quality by testing customer journey for each category, identifying authoritative data source by devices and setting up data validation methodology • Collaborated with Software Engineers to track MoM high value actions (HVA) clickstream events, identified anomalies, increased HVA source data coverage after root-cause analysis, which improved accuracy in new metrics creation and deep dive analysis related to customer journey or PV website & app traffic drivers
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