Vetted Database Design Professionals

Kunj Amrutbhai Patel

Screened

Mid-level Software Engineer specializing in distributed backend systems on AWS

Seattle, WA4y exp

“Built production systems in the AWS ecosystem, including an internal AI assistant for diagnosing account transfer and permissions issues and an end-to-end account transfer workflow used by enterprise customers. Stands out for combining LLM/RAG design with strong distributed systems reliability practices, emphasizing guardrails, fallbacks, and operational trust in high-stakes workflows.”

“Deloitte consultant who led cross-functional teams delivering a Snowflake/AWS data ingestion, warehousing, and analytics platform, with a strong track record of executive alignment and risk mitigation. Built reusable business-development accelerators (including an end-to-end Appian app and a Java integration-config tool) credited with helping secure $75M+ in contracts, and has high-confidentiality experience consulting for DoD and FDA.”

Vatsal Gabani

Screened ReferencesStrong rec.

Mid Software Engineer specializing in backend systems and AI-enabled platforms

California, USA4y exp

“Full-stack engineer with hands-on ownership of a support ticket intelligence platform, spanning React/TypeScript frontend work and backend API, PostgreSQL, Redis, and Docker-based deployment. They stand out for driving practical architecture and performance improvements in production, including moving heavy processing async and cutting response times from about 300ms to 150ms while improving reliability.”

Matthew Allyn

Screened ReferencesStrong rec.

Executive engineering leader specializing in enterprise architecture, cloud, AI, and FinTech

Rolesville, NC24y exp

“Engineering leader with experience modernizing e-commerce platforms and leading product strategy for data-driven franchise growth tools. Notable for scaling distributed teams, making pragmatic build-vs-buy and architecture decisions, and delivering measurable business impact including dramatically faster checkout performance and revenue gains.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”

Johnny Davis

Screened

Senior Enterprise Customer Success Manager specializing in SaaS adoption and digital transformation

San Jose, CA18y exp

“Enterprise Customer Success leader (10+ years) with experience at Zscaler and supporting Fortune 500 customers, driving high adoption and renewals through metric-driven governance and executive stakeholder management. Known for cross-functional incident leadership (including coordinating with Apple and Product to ship a patch) and for enabling significant expansions (e.g., $600k+ add-on before renewal and a $750k DLP deal) while building Salesforce Marketing Cloud/CRM analytics and enablement tooling.”

“Solaris-focused engineer who has led production performance investigations using DTrace and implemented code changes that substantially improved runtime by removing redundant parsing. Participated in a data-center-exit lift-and-shift to cloud using Oracle GoldenGate for replication and LVM to duplicate the application platform, with some exposure to AIX/IBM Power (LPAR recovery with NIM, assisted PowerHA validation).”

Likhitha Bethi

Screened

Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI

Stony Brook, NY4y exp

“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”

“Built both traditional data infrastructure and LLM-powered product workflows, spanning a Python/SQL ETL deployment at Amazon and an adaptive learning system for their DataLingo platform. Particularly interesting for roles at the intersection of data engineering, applied AI, and customer-facing product delivery, with hands-on experience stabilizing probabilistic LLM systems in production.”