At Foodies Takeout & Delivery—a Florida-based food ordering and delivery platform—I contributed across development, design, DevOps, and strategic discussions to build a high-performance, multi-tenant customer engagement and order management system that fuses CRM-level customer intelligence with CMS-grade operational control. I engineered MongoDB aggregation pipelines and query optimizations that cut API latency by up to 65%, reduced infrastructure costs 20–30%, and improved segmentation precision for targeted marketing by 12–18%. On the backend, I implemented versioned, tenant-specific API integrations achieving >99.5% sync reliability, reduced MTTR by up to 40%, and delivered secured, aggregation-driven customer intelligence endpoints that lowered CAC by 12% and boosted campaign conversion projections by 18–25%. On the frontend, I refactored 13+ React routes with lazy-loaded modules, reducing initial payloads by over 1 MB and improving Time-to-Interactive by up to 50%, while orchestrating type-safe async Redux flows to eliminate loader race conditions and improve response times 30%. From a DevOps and reliability standpoint, I introduced structured logging, SLO tracking, and failover-resilient media/event delivery pipelines that improved incident triage speed by 50% and achieved near-perfect event capture rates. This combination of deep technical optimizations and measurable business impact enabled Foodies to scale operations efficiently, enhance customer engagement, and strengthen revenue growth—all from a unified, high-efficiency platform. All work and interactive previews (Figma, video demos, social profiles) are documented in Notion.

Media

Project Points Earned

  1. High-Performance CRM & Revenue Intelligence – Engineered MongoDB aggregation pipelines (\$match → \$group → \$project) for real-time RFM (Recency, Frequency, Monetary) analytics, improving segmentation precision by 12–18%, reducing enrichment latency 30–45%, and cutting CRM API costs up to 65% via idempotent PATCH operations.
  2. End-to-End API & Query Optimization – Refactored core endpoints with early \$match filters, \$facet-based pagination, lean projections, and O(1) lookups, reducing DB round trips by 50%, API latency by up to 65%, and Node.js heap usage by 75%, yielding 20–30% infra cost savings.
  3. Multi-Tenant, Versioned API Integrations – Standardized outbound requests with versioned headers & tenant-specific tokens, achieving >99.5% sync success rate and reducing MTTR (Mean Time to Recovery/Repair) by 25–40% for integration incidents.
  4. Aggregation-Driven Customer Intelligence APIs – Delivered secured visit-count and last-order analytics endpoints, boosting campaign conversion projections by 18–25% and reducing CAC (Customer Acquisition Cost) by up to 12%.
  5. Advanced Search & Computed Field Indexing – Implemented \$expr/\$regexMatch full-text search on computed fields, improving query recall by up to 45% while maintaining indexed performance and reducing p95 latency by 25%.
  6. Lifecycle-Aware Coupon & Campaign Analytics – Normalized redeemed/rewarded states via conditional aggregation, increasing cohort accuracy by 10–15% and enabling precision targeting in promotions.
  7. React Route-Level Code Splitting – Refactored 13+ pages to lazy-loaded modules, cutting initial JS payload 0.5–1.2MB, improving TTI 30–50%, and enhancing Core Web Vitals.
  8. Redux Async Orchestration – Built type-safe createAsyncThunk flows with centralized loading/error states, reducing request fan-out, improving response times by 30%, and eliminating loader race conditions.
  9. Timezone-Aware Data Pipelines – Implemented region-specific cron scheduling, eliminating 70% of replay jobs and 60% of burst API errors due to timezone drift.
  10. Operational Observability & MTTR Reduction – Introduced structured logging with dedicated instrumentation, improving incident triage speed by 50% and enabling actionable SLO (Service Level Objective) tracking.
  11. Network Efficiency & Client-Side State Optimization – Reduced redundant API calls by 50–70% through client-side metric toggling and debounced server pagination, cutting QPS (Queries Per Second) during peak bursts.