Project Overview
Refactored scaling web architecture with modular service boundaries, automated CI/CD deployments, container orchestration, and full-stack telemetry.
The Operational & Engineering Challenge
Feature velocity was outrunning architecture. Monolithic database queries were causing intermittent latency spikes, manual production deployments carried high risk of downtime, and lack of observability made diagnosing errors slow.
- Zero downtime migration for existing active users.
- Reproducible infrastructure defined entirely in code (IaC).
- Strict database backup and disaster recovery protocols.
Engineering & Architecture Approach
We designed a modular cloud architecture utilizing Docker containers, Terraform infrastructure-as-code, and automated GitHub Actions delivery pipelines. Added Redis caching layers, connection pooling, and distributed Datadog observability across all critical service boundaries.
100% of AWS cloud resources codified in modular Terraform templates.
Automated linting, test suites, container builds, and zero-downtime blue/green deployments.
Read replicas, connection pooling with PgBouncer, and index refactoring.
Structured JSON logging, APM tracing, and automated alert thresholds.
Key Engineering Decisions
Separated compute and persistent storage layers to allow independent auto-scaling.
Implemented preview staging environments on every pull request to catch regressions before merge.
Added automated database snapshots and point-in-time recovery testing.
Verified Outcomes
Zero-downtime automated production deployments.
Drastically reduced median API response latency under peak load.
Engineers can ship and verify changes with complete confidence in automated rollbacks.
