Alex Chen

Lead backend engineering for core platform services. Architect and build high-throughput microservices. Mentor junior engineers and drive technical excellence across teams.

Real-Time Payment Processing Platform

• Built a real-time payment processing platform handling $2B+ annually. Replaced legacy monolith with event-driven microservices architecture achieving 99.99% uptime and sub-100ms p99 latency.
• Event-driven microservices with Kafka, PostgreSQL, Redis, and Spring Boot on AWS EKS
• Led architecture design, implemented core payment orchestration service, established CI/CD pipelines, and defined observability standards.
• Technical Stack: Spring Boot Kafka PostgreSQL Redis AWS EKS gRPC Prometheus Grafana
• Challenges: Latency Reduction: Reduced payment processing p99 latency from 800ms to 85ms through async event processing and query optimization.
• Challenges: Throughput Scaling: Scaled platform to handle 50,000 TPS during peak traffic with auto-scaling and backpressure mechanisms.
• Challenges: AI-Assisted Development: Integrated Claude Code and GitHub Copilot into development workflow for test generation, PR review assistance, and documentation updates with human validation gates.
• Challenges: Cost Optimization: Reduced AWS infrastructure costs by 35% through right-sizing, spot instances, and intelligent caching strategies.

Fraud Detection Engine

• Developed real-time fraud detection engine processing 100K+ transactions per second using machine learning models integrated with streaming pipelines.
• Streaming architecture with Apache Flink, Kafka, TensorFlow Serving, and Spring Boot microservices
• Designed streaming pipeline architecture, implemented feature engineering service, and built model serving infrastructure with A/B testing framework.
• Technical Stack: Apache Flink Kafka TensorFlow Serving Spring Boot DynamoDB Kubernetes Jaeger
• Challenges: Model Latency: Achieved sub-20ms model inference latency through batching, caching, and optimized model serialization.
• Challenges: Detection Accuracy: Improved fraud detection accuracy by 22% through real-time feature engineering and model ensemble techniques.
• Challenges: Observability: Built comprehensive tracing and metrics pipeline enabling rapid debugging and regression detection.

Developed and maintained enterprise data platform services. Built RESTful APIs and batch processing pipelines for data analytics customers.

Customer Data Platform

• Built unified customer data platform consolidating data from 15+ sources into actionable customer profiles for marketing and sales teams.
• Batch and streaming hybrid with Spring Boot, Apache Spark, Snowflake, and Airflow on Azure
• Implemented data ingestion pipelines, built REST APIs for profile access, and developed data quality validation framework.
• Technical Stack: Spring Boot Apache Spark Snowflake Airflow Azure JUnit Mockito
• Challenges: Data Quality: Reduced data quality issues by 60% through automated validation and anomaly detection rules.
• Challenges: API Performance: Optimized API response times by 40% through query tuning, caching layer introduction, and connection pooling.