GovChime Analytics Platform
Government Contracts Intelligence
Sole engineer owning a 7-package monorepo: Express.js API, Next.js on Cloudflare Workers, SLED Admin on Cloudflare Pages, and 3 SmartSync microservices. 50+ materialized views across PostgreSQL and ClickHouse OLAP on 70M+ rows. End-to-end CI/CD pipeline with 24+ workflows (unit → integration → E2E → production deploy) on a self-hosted runner.
Key Features
- 7-package monorepo: Express.js API, Next.js frontend, SLED Admin, 3 SmartSync microservices
- Next.js on Cloudflare Workers with ISR, SLED Admin on Cloudflare Pages
- 50+ materialized views across PostgreSQL and ClickHouse OLAP on 70M+ rows
- 24+ CI/CD workflows: unit → integration → E2E → production deploy
- AI pipeline for data sanitization, contract matching, and description generation
- Stripe paywall, PostHog analytics, and self-hosted runner infrastructure
Tech Stack
Backend
Frontend
Database & OLAP
Infrastructure
AI & Dev Tools
Challenges & Solutions
Slow Analytics Queries on 70M+ Rows
Real-time aggregation queries across 70M+ rows with complex JOINs took seconds, making dashboards unusable for end users.
Designed ClickHouse OLAP integration alongside PostgreSQL with 50+ materialized views for common aggregations. Query times reduced by 100-300% — dashboards became instant.
Multi-Service CI/CD for Sole Engineer
7 packages with interdependent builds and deploys needed reliable CI/CD without a dedicated DevOps team. Frontend ISR depends on backend being live, services must deploy atomically.
Architected 24+ GitHub Actions workflows on a self-hosted runner with dynamic port allocation, Komodo HTTP API for Docker orchestration, and a Build → Verify → Deploy pipeline ensuring frontend validates against temp backend before any production deploy.
Data Quality at Scale
Raw SamgovAPI data contained inconsistencies, missing fields and unstructured descriptions making it difficult to search and match contracts.
Built AI pipeline using LLM APIs for automated data sanitization, opportunity matching and description generation. Structured output validation ensures consistent data quality.