Vetted Error Handling Professionals

“Built a 24/7 Python/LangChain email agent in production with validation, circuit breakers, human-review escalation, and structured observability. Also applied data and automation skills at Community Dreams Foundation, including turning a vague donor-insights request into a usable donor-risk prediction workflow and raising ETL reliability from roughly 85% to 99% by diagnosing SQLite concurrency issues.”

Michael Forster

Screened ReferencesStrong rec.

Senior Data Engineer specializing in ETL/ELT pipelines and data integration platforms

New York, NY15y exp

“Data engineer/software engineer who led an end-to-end ETL/ELT pipeline at Pearson processing millions of rows of student data nightly, including client-side data prep/validation, SFTP/API ingestion, staging-based SQL validation/transforms, and production loading. Built reliability features like configurable per-client validation thresholds, detailed reporting, concurrency throttling via a custom queue, and multi-source merge/backfill logic to keep nightly loads running even when sources fail.”

“QA professional from fintech/SAP security and complex identity systems who has owned end-to-end testing across the SDLC, including being the sole QA on a high-risk payment platform carrier migration. Demonstrated strength in integration testing, data integrity validation, and diagnosing calculation/automation defects using controlled test data and scripted date emulation; experienced with JIRA/TestRail and Selenium-based regression coverage.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Full-stack engineer with strong Next.js App Router/TypeScript experience who built production dataset search/filtering and data-heavy dashboards backed by Postgres. Demonstrates hands-on performance work across the stack (EXPLAIN ANALYZE, composite indexes, caching, React profiling/memoization) and has built durable, Temporal-like orchestrated data-processing workflows with idempotency and retry strategies in an early-stage startup environment (Gaia AI).”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”

“Backend engineer with experience in fraud reporting and billing systems, building Java/Spring Boot services behind a React frontend and improving performance 40%+ with caching and SQL optimization while maintaining 99.9% uptime. Has hands-on experience migrating a monolith to microservices with incremental rollout, clear data ownership boundaries, and production-grade API reliability/security practices (JWT/OAuth, RBAC, row-level scoping).”

“Backend-focused Python engineer who has owned production FastAPI services deployed on Kubernetes, including CI/CD (GitLab CI to ECR) and GitOps delivery via ArgoCD/Helm. Has hands-on experience with complex reliability and infrastructure work—solving data inconsistency with validation/partial-data paths, fixing K8s liveness issues via lazy loading, and supporting a phased cloud-to-on-prem migration with dual-writes and monitoring. Also built Kafka-based real-time ingestion consumers handling bursty, high-throughput traffic with async processing and topic/retention tuning.”

“Cloud/ML engineer with production deployment experience on Azure (Dockerized models, managed APIs, data pipelines) who has repeatedly stabilized unreliable systems—e.g., taking an API-driven analytics pipeline from ~60% to 98% reliability and an Azure ML service from ~80% to 97% by addressing rate limits, container memory, and gateway timeouts. Also built an explainable contract-risk model for entertainment bookings (Transformers + SHAP) and integrated it into a legacy booking system via a Flask REST API, plus prior IoT work at Nissan processing CAN bus sensor streams for diagnostics/anomaly insights.”