Stephen Chen

Led development of Meta's Privacy Goal Ingestion Platform using Go, GraphQL, Kubernetes, MySQL, and distributed microservices, reducing privacy-compliance onboarding effort by 60% across engineering organizations. Architected and implemented a centralized self-service permission management platform using Go, React, GraphQL, and backend authorization services, reducing manual access-review effort by 75%. Designed and developed highly available backend services processing millions of authorization and compliance requests while maintaining 99.99% service availability. Built full-stack privacy-compliance dashboards using React, GraphQL, Python, SQL, and distributed backend services, reducing executive reporting effort by 80%. Improved reliability via Datadog, Prometheus, distributed tracing, automated alerting, and observability tooling, reducing production incidents by 40%. Partnered with legal, privacy, security, and infrastructure teams to implement scalable policy-enforcement solutions

Led development of Meta's Privacy Goal Ingestion Platform with Go, GraphQL, and Kubernetes microservices to streamline privacy compliance and engineering governance. Architected centralized permission management, reducing manual access-review effort by 75% and improving secure authorization. Built highly available backend services processing millions of authorization and compliance events for secure enterprise analytics and AI/ML access. Developed React/GraphQL/Python/SQL dashboards for real-time operational insights and compliance metrics. Led migration from Apple SKAdNetwork to Aggregate Event Measurement for privacy-preserving attribution. Designed distributed event-processing pipelines for billions of advertising events powering analytics, experimentation, recommendation systems, and downstream ML workflows. Implemented data quality frameworks with automated validation, anomaly detection, and large-scale data correction to reduce recovery time by 77%. Collaborated across Data Scien

Led reliability initiatives for Meta's migration from Apple SKAdNetwork (SKAN) to Aggregate Event Measurement (AEM) using Go, Python, SQL, and distributed event-processing systems supporting large-scale advertising attribution. Designed and implemented large-scale data-correction pipelines using Go, Python, SQL, analytics systems, and distributed processing frameworks, reducing data recovery time by 77% from 30 days to 7 days. Developed backend services and data validation frameworks supporting privacy-preserving advertising measurement and analytics workloads. Built automated anomaly-detection and data-quality validation systems using Python, SQL, and analytics tooling, reducing manual operational effort by 70% while improving measurement accuracy. Led multiple SEV-1 and SEV-2 incident response efforts using monitoring, distributed tracing, observability tooling, and operational automation to help prevent an estimated $5M+ revenue impact. Enhanced observability via Datadog dashboards,