Projects
I build systems that start with real business constraints and end with testable engineering requirements. These projects focus on turning social engagement into structured data so small businesses can make better decisions.
SMS Redirect
Convert inbound SMS into trackable traffic and measurable engagement events.
SMS Demand Signal
Pre-launch demand capture that helps brands forecast SKU interest before a drop.
Full-Stack Auth Platform
JWT authentication and user management built with React, Express, and PostgreSQL.
SMS Redirect
Turning “someone texted us” into measurable, attributable engagement.
Business Requirements
- Capture engaged customers from social media without relying on platform algorithms.
- Measure promotion performance beyond likes/comments (attribution + engagement).
- Build an owned channel for future promotions (opt-in only).
- Make results visible enough to improve campaigns over time.
Engineering Requirements
- Inbound SMS webhook to parse keywords and associate them with a campaign.
- Redirect endpoint that logs clicks as immutable events (timestamped).
- Unique user tracking (phone hash or normalized identifier) for funnel metrics.
- Consent capture with stored timestamp and campaign association.
- Relational schema designed for reporting queries (CTR, opt-in rate, uniques).
System Flow
Social Post → SMS Keyword → Webhook (log inbound) → Auto-reply with branded link → Redirect endpoint (log click) → Landing page → Consent capture → Analytics aggregation
Core idea: treat every interaction as an event so the business can measure funnels instead of guessing.
Implementation
- Frontend: Astro landing pages (fast, static, SEO-friendly)
- Backend: Node.js + Express webhook + redirect service
- Database: PostgreSQL event schema (campaigns, users, events, consent)
- Deployment: Render with environment-based configuration
Testing Requirements
The key risk is inaccurate attribution and dirty event data. Testing focuses on integrity and end-to-end realism:
- Webhook payload validation (missing fields, malformed numbers, unknown keywords)
- Redirect logging verification (campaign id, timestamps, idempotency rules)
- Duplicate prevention (same user repeated events, spam scenarios)
- Consent enforcement (must be explicit; record timestamp; support opt-out logic)
- Integration test: simulate SMS → link → redirect → landing → consent
- Data checks: unique counts, foreign keys, required fields, query correctness
Business Outcome
- Creates an owned audience (opt-in list) instead of ephemeral social views.
- Enables measurable campaign performance (unique users, click-through, opt-in conversion).
- Supports data-driven follow-up promotions (discounts to engaged segments).
SMS Demand Signal
Pre-launch demand capture to reduce inventory risk and improve drops.
Business Requirements
- Estimate demand before ordering inventory (reduce unsold stock).
- Understand which designs/SKUs are most wanted, early.
- Capture opt-in users for future drop notifications and discounts only.
- Make decisions based on behavior (clicks/intent) rather than vanity metrics.
Engineering Requirements
- Drop preview page with item-level interaction tracking (SKU clicks).
- Event ingestion endpoint to store interest signals with timestamps.
- Consent capture (opt-in) with auditable timestamp + campaign/drop association.
- Aggregation endpoint for reporting: top SKUs, unique interest, opt-in conversion.
- Schema designed to answer questions quickly (items, interest, drops, users).
System Flow
Instagram → SMS trigger → Drop preview page → SKU click logged → Consent capture → Demand aggregation → Decisions (order quantities, promos)
This treats “interest” as a demand signal and connects it to an owned messaging channel.
Implementation
- Frontend: Astro drop preview pages + lightweight click tracking
- Backend: Node.js ingestion service + analytics endpoint
- Database: PostgreSQL schema (drops, items, item_interest, consent)
- Deployment: Render
Testing Requirements
The risk here is misleading analytics (bad aggregation, duplicate events, incorrect uniques). Testing targets correctness:
- Click event capture integrity (SKU id present, timestamps, drop association)
- Duplicate/rapid clicks behavior (throttle or store with rules)
- Consent edge cases (must be explicit; record timestamp; allow opt-out)
- Aggregation query verification (top SKUs, unique users, conversion rates)
- Integration test: SMS link → preview page → click events → consent → analytics
- Load simulation: high-volume clicks + ingestion stability
Business Outcome
- SKU-level demand forecasting before inventory spend.
- Better drops by prioritizing high-intent designs.
- Targeted promotions (discounts) to opted-in users with demonstrated interest.
Full-Stack Auth Platform
A production-style authentication foundation with JWT-based login, secure password handling, and a PostgreSQL-backed user model. Built to demonstrate clean API design, validation, and extensibility for role-based access.
Highlights
- JWT authentication + session handling patterns
- User registration with validation + duplicate prevention
- PostgreSQL schema + migration-friendly structure
- Containerized local dev (Docker / Compose)
- API testing workflows (e.g., Postman) + error handling