AWS & Serverless Applications

The Problem

A technical team needed backend automation and API infrastructure that could handle unpredictable load — jobs that ran occasionally but needed to complete reliably, APIs that had to scale with usage spikes, and integrations with third-party services that required durable retry logic. Running traditional servers meant paying for capacity they didn't consistently use and maintaining infrastructure that wasn't their core focus.

What I Built

Architected Lambda-based serverless applications covering background jobs, event-driven workflows, and REST APIs via API Gateway. Used CloudFormation to define all infrastructure as code for repeatable deployments across environments. Designed DynamoDB schemas and S3 integrations for storage and processing pipelines. Set up CI/CD pipelines so deployments required no manual steps after a code push.

Technical Challenge

Lambda's stateless execution model requires deliberate design when jobs need to coordinate or track state across invocations. For a multi-step processing pipeline, this meant building a DynamoDB-backed state machine that could resume interrupted jobs without reprocessing completed steps or losing data — rather than relying on in-memory coordination that doesn't survive a function timeout or cold start.

What Was Delivered

• Lambda-based serverless API and background job architecture
• API Gateway configuration with authentication and rate limiting
• DynamoDB data modeling and access pattern design
• S3 integration for file storage and event-driven processing
• CloudFormation infrastructure-as-code for all environments
• CI/CD deployment pipeline with environment promotion
• Event-driven workflow orchestration with retry and failure handling

Outcome

Infrastructure cost dropped compared to equivalent reserved compute. Deployments went from a manual checklist to a fully automated pipeline triggered by a code push. The team stopped spending time on server maintenance and focused on product work instead.

Technologies