Vetted Database Schema Design Professionals

Tony Barreto

Screened ReferencesModerate rec.

Mid-Level Full-Stack Software Developer specializing in modern web apps

San Francisco, CA5y exp

“Product-focused full-stack builder who has shipped and operated multiple production apps from scratch, including an e-commerce bakery delivery scheduler (with concurrency controls and timezone handling) and a real-time passenger music-request system for Lyft rides that hit and resolved YouTube API rate-limit scaling issues via debouncing and caching. Strong in React+TypeScript and Node.js/TypeScript backends, with solid PostgreSQL/PostGIS data modeling and performance tuning.”

Yeruva Bala Shreya Reddy

Screened

Entry Data Analyst specializing in ETL pipelines and business intelligence

Charlotte, NC0y exp

“Analytics candidate with hands-on experience building reliable healthcare reporting layers from messy transactional data using SQL and Python. Stands out for combining data transformation, KPI definition, validation rigor, and performance tuning to deliver reusable reporting assets that improve trust in operational metrics.”

Krystal Galdamez

Screened

Junior Full-Stack Software Engineer specializing in AI-powered web applications

San Jose, CA1y exp

“Startup-focused engineer who has shipped Python backend features, AI integrations, and Playwright automation for products including an AI coaching platform and hiring workflow tools. Stands out for working through ambiguous zero-spec environments, hardening flaky Firebase-authenticated test flows, and designing practical fallback paths when AI outputs are unreliable.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“Full-stack engineer from an early-stage AI SaaS startup who owned and shipped a production AI-powered PDF document chat and sharing feature end-to-end (React/TS + Node + Postgres on AWS). Demonstrates strong product thinking through layered success metrics and tight feedback loops, plus hands-on reliability/observability work (CloudWatch, structured logging, alarms) and robust ingestion pipeline patterns (idempotency, retries, reconciliation).”

“Built and shipped a production LLM feature that explains DSA problems with real-life explanations, using Grok with automatic failover to OpenRouter (and multiple backup models) to avoid user-facing failures. Improved cost efficiency by implementing difficulty-based token budgets and iterated prompt quality via structured constraints and an in-app feedback mechanism, reporting satisfaction across 38 users.”

“Built and productionized an LLM-powered service desk ticket triage and reporting agent that classifies, prioritizes (including sentiment/urgency), and summarizes tickets into structured SQL outputs feeding Power BI dashboards. Emphasizes production reliability (99% uptime) with retries, schema validation, confidence thresholds, human review queues, and rule-based fallbacks, delivering 85–90% reduction in manual effort and 25–30% faster resolution times.”