Vetted SQL Query Optimization Professionals

“Backend/data engineer with healthcare/telehealth domain experience, building patient appointment and data-processing systems on AWS. Has delivered production microservices and ETL pipelines (Flask/Celery, Glue/PySpark) with strong reliability/observability practices (JWT, retries/timeouts, Sentry/CloudWatch) and modernization experience migrating SAS workflows to Python services, including a documented 10min→30sec SQL performance win.”

“Technology/architecture leader with Accenture experience delivering data- and AI/ML-driven products, including a legal contract search solution and customer sales analytics for AWS. Known for scaling distributed teams (onshore/offshore), making pragmatic architecture decisions, and solving hard data problems (proprietary sources, data quality) while implementing scalable integrations like Redshift-to-Salesforce via parallelized pipelines.”

“Backend/AI engineer who built a production GPU-backed real-time inference API at Nvidia and debugged burst-induced tail latency, cutting P95 by ~29% through dynamic batching and backpressure. Also shipped an end-to-end RAG + agentic operational diagnostics assistant with strict tool controls, evidence citation, confidence gating, and strong production guardrails, plus demonstrated hands-on Postgres optimization (900ms to 40–60ms).”

“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”