Staff-level Backend, Database and AI Infrastructure Engineer

Solve advanced PostgreSQL challenges involving database architecture, performance tuning, and high-availability design.

Provide remote, part-time consulting support for production database systems.

Built and operated production observability for large-scale distributed services using Prometheus, Grafana, and OpenTelemetry instrumentation.

Authored Kubernetes operators and custom resources to automate lifecycle management for stateful workloads. Managed Helm-based releases across multiple clusters.

Led two high-load projects as part of a monolith-to-microservices migration for a sponsored products team. Redesigned, redeployed, and maintained distributed SQL Server database support to improve soft-delete behavior.

Introduced parallel integration testing, reducing weekly deployment time by 80% across internal sub-teams. Built development, CI, and test environments that mirrored production Kubernetes behavior for more than 100 engineers.

Implemented ETL features and enabled seamless ingestion of data for new tenants.

Redesigned and deployed a multi-tenant distributed PostgreSQL database architecture, reducing missing components across tenants by 37% and minimizing supply-chain disruptions.

Developed and enhanced a Python-based file parser that accelerated data ingestion by 32 times. Owned the end-to-end back-end from inception using Python, Flask, and PostgreSQL.

Built a property-comparison algorithm with PostGIS and RabbitMQ that processed three million cases in under three hours and generated cached client-ready data. Supported a web application helping customers reduce property tax by approximately 10% on average.

Participated in Agile development on existing GitHub issues involving coding, refactoring, and debugging.

Completed approximately 100 GitHub issues using Python, PostgreSQL, and TypeScript.

Delivered multiple software projects as a Top Rated Plus developer with flawless five-star ratings.

Used Amazon Web Services to meet and exceed client requirements.

Self-taught Python and the Pandas library to conduct analysis of CSV datasets containing more than 100 million rows.

Supported an international airport construction project valued at approximately $2 billion.

Owned logging operations, including observability and high-availability responsibilities.

Worked directly with clients, answering questions and providing real-time solutions.