Vetted Distributed Systems Professionals

“Worked on an AWS DynamoDB Journal team project building internal operator dashboards end-to-end, creating Java/Spring Boot APIs and integrating them into a Spring Boot/Thymeleaf/JavaScript UI to speed up debugging workflows. Has experience with data-heavy web apps and performance techniques (load balancing, caching, pagination, compression) plus hands-on debugging across unit/integration/E2E tests; also maintained and enhanced a React website at Global Spark.”

Jim Woodroffe

Screened

Executive Engineering Leader (CTO/VP) specializing in scaling platforms, AI, and cloud infrastructure

San Francisco, CA24y exp

“Entrepreneurial operator who previously built an AI context layer for personal communication/relationships and has worked with a New York-based venture group to evaluate early-stage ideas from both technical and business angles. Currently exploring broad startup opportunities and collaborating with 2–3 early-stage companies, with a structured approach to assessing market outcomes, competition, and team fit.”

“Technology leader from Franklin Templeton who modernized wealth management tech by launching client-facing digital web/mobile experiences via a phased data-warehouse-to-real-time strategy, while aligning internal CRM visibility for front office teams. Scaled an engineering org from 15 to ~250 (15 teams) and drove a shift from waterfall to Agile with microservices/micro-frontends and Jira-based transparency to improve delivery velocity.”

“Engineering manager/player-coach in the Alexa media ecosystem who led delivery of a Tier-1 gateway service enabling third-party providers (e.g., Spotify/Pandora) to control playback across users’ Echo devices, driving 10–15% lifts in engagement metrics. Deep in API design, AWS infrastructure choices (EC2 vs Fargate), OAuth/LWA auth flows, and production rigor (security reviews, load testing, feature-gated dial-ups), with demonstrated incident ownership and operational automation wins.”

“Google Health engineer who owned and shipped an AI-powered clinical insights dashboard and NLP clinical note extraction service end-to-end (React/Next.js frontend; Python/Node microservices on GKE; TensorFlow transformers; BigQuery analytics). Demonstrated strong production rigor (CI/CD, testing, observability, guardrails for sensitive data) and delivered measurable outcomes including 30% faster diagnostics, 40% less manual documentation, 15% higher adoption, and 25% lower ops costs.”