Hey, it's Nam

Building release automation for RDS/Aurora database engines. Turns out shipping a database engine is mostly about not breaking the ones already running.

• Architected a release orchestration framework for RDS/Aurora MySQL and MariaDB engines, modeling releases as config-driven DAG workflows with a poll-based reconciler to catch silent failures.
• Designed a ports-and-adapters architecture around a crash-safe reconciler to keep the framework engine-agnostic, running in production across 20 release scopes and 7 engine versions, cutting release cycle time from 14 days to 4 days.
• Built an LLM commit classifier judging cherry-pick portability across database LTS branches using Claude via Bedrock, with a deterministic git filter and human-in-the-loop review, cutting the number of commits to review by 5 times.
• Fixed a reconciliation race condition where a running automated action could silently overwrite an operator override, re-ordering the action sequence so human decisions always take precedence over in-flight actions.
• Built a real-time dashboard to track, control, and manage multiple concurrent MySQL and MariaDB releases, with automated Slack reporting that replaces hours of manual status tracking across platforms.

Worked on App Store data and localization, and learned how to find my way around a very large Rails monolith.

• Delivered the App Store web vitals collection pipeline, routing events from ClickHouse for 21k+ App Store apps into a unified analytics stream to replace the Google Data Platform pipeline, improving data alignment from 80% to 94%.
• Developed the service layer, including a repository and API facade, to extract web vitals metrics from the app runtime events pipeline into the developer dashboard, driving alignment across teams from requirements and prototype to delivery.
• Built an event-driven Kafka pipeline for App Store LLM localization across 19 languages, processing 150+ translations/day with fault-tolerant retry and progressive rollout, improving self-serve rate by 12% for 450+ daily setups.
• Introduced an abstract translation concern to centralize request lifecycle, retry logic, and source text extraction, simplifying the translation pipeline for all App Store products and services.
• Redesigned the App Store's data layer by normalizing the app listing model and using MySQL listing tables for all presentation data, reducing Elasticsearch query overhead and keeping p99 query time and App Store SLOs consistent.
• Ranked top 21% in AI tool usage across Shopify by token efficiency across Claude Code, Cursor, and internal LLM tools, using agentic workflows to accelerate multi-file feature delivery in the Rails monolith.

Built internal tooling for barcode validation, and learned how much geometry hides behind a scan.

• Engineered a cross-platform image processing pipeline in Python using SSH/SCP for remote data ingestion, cutting analysis time by 70% and replacing a 500+ line legacy script.
• Shipped a barcode analysis platform with CLI and GUI interfaces for image comparison, saving 15+ engineering hours weekly on manual result aggregation and comparison workflows.
• Redesigned a C++ barcode validation algorithm using polygon intersection geometry, reducing the false detection rate by 17%.
• Simplified a legacy Python data analysis tool with an MVC design pattern to achieve an 80% code reduction (from 2000+ LOC), using Pandas to accelerate processing on large datasets.

Designed an evaluation system to validate rental listings in BC. Learned more about real estate in BC than I expected to.

• Designed and developed a rental compliance validation engine in Django with PostgreSQL, evaluating 1k+ Airbnb listings against municipal policies through automated rule-based logic and data pipeline processing.
• Implemented multi-stage Docker builds with layer caching to cut build times and standardize environments across the team.
• Boosted sign-up conversion rate by 21% by building the Next.js landing page with a simplified sign-up flow and UX improvements.
• Reduced code review time by 80% and achieved 90%+ test coverage by establishing a Jest testing framework for the team.

Developed features for a health monitoring device. Learned how to connect ventilators to patients.

• Improved system reliability by developing real-time alarms with C++ circular buffer optimization and configurable thresholds.
• Developed and published a Python CLI tool packaged with Poetry to truncate and compare patient data from CSV files, saving 5 hours/week of data cleaning and pre-processing.
• Enhanced the file compression and download process in C++ for a Linux application with real-time file selection and log timestamp correlation, improving data coverage and reducing download time by 23%.
• Enhanced data analysis efficiency by 29% by developing Python automation scripts for clinical parameter verification.