Vetted Snowflake Professionals

“Backend/data engineer with experience on large-scale consumer platforms (Google and Meta), building high-traffic Python microservices (REST/gRPC) on Kubernetes with strong reliability/observability practices. Delivered AWS container-based deployments with CI/CD and IaC, and built AWS Glue ETL pipelines on S3 with schema evolution and data quality controls; also has demonstrated SQL tuning impact (15% latency reduction) and incident ownership for batch pipelines.”

“Meta engineer who owned end-to-end production systems for AI-enabled smart glasses, spanning React/TypeScript apps through Node/Java microservices on AWS EKS with Kafka/Postgres. Built and productionized a real-time RAG pipeline (LangChain + OpenAI + Elasticsearch) with rigorous guardrails (shadow/canary, fallbacks, monitoring), delivering major improvements in latency (~35–40%), error reduction (~30%), and engagement (reported +40% DAU).”

“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.”

“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.”

“Senior engineering/technology leader who has defined and delivered a multi-year roadmap to modernize platforms and embed AI, leading global teams through cloud-native and microservices migrations on AWS/Azure. Demonstrated measurable outcomes including 99.99% uptime, 40% fewer incidents, 25% faster delivery, 5x scalability, and $30M in new business opportunities, while scaling a 100+ person distributed org with strong OKR-driven execution and mentorship culture.”

“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”

“McKinsey & Company ML/NLP practitioner who builds production-grade AI systems across sectors (notably healthcare and finance), including RAG/LLM solutions, entity resolution pipelines, and embedding-powered search with vector databases. Demonstrated measurable impact (40% reduction in data duplication) and strong MLOps/data workflow practices (Airflow, MLflow, Spark, AWS/GCP, Prometheus, CI/CD).”