David Tan

Architected and led the development of a microservices-based backend in Node.js and Python, supporting millions of global users and handling hundreds of thousands of requests per minute; migrated a legacy monolith into scalable services, improving reliability by ~40%. Designed and deployed a real-time data pipeline on AWS (Lambda, SQS, DynamoDB, RDS), enabling low-latency ride matching and ETA predictions. Implemented CI/CD pipelines with automated testing (unit, integration) using GitHub Actions and Terraform, delivering a 30% faster deployment cycle and reduced error rate. Integrated machine learning inference (Python + TensorFlow) into the backend to improve driver pickup prediction accuracy, reducing no-show rates by ~12%. Mentored a team of 6 engineers, conducted architecture reviews, and facilitated cross-functional planning with product and data science teams to improve delivery predictability and code quality. Led a reliability initiative with health checks, canary deployments,

Built and maintained web services for a large-scale SaaS application using React + TypeScript on the frontend and Node.js on the backend, serving tens of thousands of enterprise users. Used Azure Cosmos DB and Azure SQL—NoSQL and SQL data stores—to support real-time and transactional features. Implemented machine learning features in collaboration with data science, building Python microservices for predictive analytics (usage forecasting), increasing user engagement by ~15%. Championed test-driven development (TDD) and designed comprehensive test suites (unit, contract, end-to-end), enabling automated deployment with Azure DevOps and CI with release gating and rollbacks, cutting release downtime by ~60%. Acted as a technical liaison between engineering and PMs to drive improvements, feature prioritization, and strategic decisions.

Built and maintained web services for a large-scale SaaS application using React + TypeScript on the frontend and Node.js on the back end, serving tens of thousands of enterprise users. Designed NoSQL (Azure Cosmos DB) and SQL (Azure SQL) data stores to support both real-time and transactional features. Implemented machine learning features in collaboration with data science – built Python microservices for predictive analytics (usage forecasting), which increased user engagement by ~15%. Championed test-driven development (TDD) and designed comprehensive test suites (unit, contract, end-to-end), achieving automated deployment with Azure DevOps, enabling continuous integration, release gating, and rollbacks — cut release-related downtime by 60%. Acted as a technical liaison between the engineering team and PMs to drive improvements, and feature prioritization — driving more strategic engineering decisions.