Resume

• Evolution. Sheets → WordPress prototype → Node/React MVP → Rails API + React/Redux + PostgreSQL + Firebase.
• Ops Impact. 90%+ auto-assignments; reduced CSR load; unified accounting & payouts; real-time visibility for dispatchers.
• Architecture. Modular, observable, API-driven with strict concurrency and SLA-aware background jobs.

Architecture (high-level).

React/Redux (Ops UI) ── REST ─▶ Rails API ── SQL ─▶ PostgreSQL (RDS)             │                       │             │                       ├─ ActiveJob / Redis (assignments, SLAs)             │                       └─ Firebase (presence, push)             │             └─ Native Apps ──▶ REST / Realtime channels (same endpoints)WordPress (CMS / Admin microsite) ── REST (content, FAQs, policy)

Key Engineering Highlights

• Concurrency. Server-authoritative holds with TTL, advisory locks for safe assignment, idempotency keys, and outbox for reliable webhooks.
• Dispatch Logic. Weighted scoring (skills, distance, acceptance, rating, load, fairness) with indexed shortlisting (O(K log K)).
• Data Model. Booking, Job, Professional, Availability, LedgerEntry; JSONB for flexible metadata; covering/partial indexes for actionable queries.
• Finance. Ledger-based payouts, reconciliation reports, immutable audit trails, and automated exports for accounting.
• Observability. Structured logs, SLO dashboards, feature flags, and canary deployments.

• Objective. Track hashtags, topics, and media posts in real-time; quantify narrative direction and sentiment for strategic reporting.
• Challenge. Enforce strict API quotas, data governance, and uptime requirements while handling volatile input volumes from multiple social sources.
• Outcome. Delivered a resilient, near real-time analytics dashboard with automated classification, region tagging, and executive-ready trend reports.

Architecture (high-level).

React/Redux (Analyst UI) ──▶ REST / WebSocket ─▶ Node.js (API Gateway)                                      │                                      ├─▶ Worker Cluster (Ingestion + NLP)                                      │     ├─ Scheduler / Token Bucket                                      │     ├─ Entity & Hashtag Extractor                                      │     ├─ Sentiment Classifier (ML microservice)                                      │     └─ Trend Detector (windowed analytics)                                      │                                      ├─▶ MySQL (Relational store)                                      ├─▶ MongoDB (Document & raw ingestion)                                      └─▶ Redis (Stream queues + cache)Visualization Layer (React D3/Chart.js) ──▶ Aggregated insights + alerts

Key Engineering Highlights

• Hybrid Storage Model. MySQL for normalized entities (topics, users, trends); MongoDB for unstructured payloads and fast search across hashtags, mentions, and language variants.
• Ingestion Engine. Multi-threaded Node.js workers using token-bucket throttling and exponential backoff to respect API rate limits under burst traffic.
• Trend Analytics. Sliding window algorithm (Δvolume/Δtime) over MongoDB aggregates; flag anomalies and “emerging narratives.”
• Classification. Hashtag clustering via cosine similarity and TF-IDF embeddings; sentiment scoring with ML microservice callable via REST.
• Pipeline Reliability. Redis Streams for buffering; replayable checkpoints to prevent data loss on failure; automatic recovery nodes.
• Frontend. Real-time dashboards in React/Redux with WebSocket feeds, sentiment heatmaps, region overlays, and alert cards for spikes.

Example Data Flow

1. Scheduler triggers fetch cycle (respecting quotas)
2. Ingestion workers pull social data (Twitter / Meta / YouTube APIs)
3. Normalization & de-duplication (hash-based)
4. Sentiment + topic tagging via ML API
5. Trend detector aggregates sliding 15-min windows
6. React dashboards update live via WebSocket stream

Performance & Resilience

• Throughput. Designed for ~5M posts/day sustained ingestion.
• Latency. Dashboard updates within < 3s of source publish.
• Scalability. Stateless Node clusters behind Nginx LB; sharded MongoDB; horizontal scale workers via PM2 + queue depth monitoring.
• Governance. Role-based access control, audit trails, and retention policies as per compliance mandates.

Sentiment Detection (simplified pseudo-code)

# Token-bucket throttled request + ML classification
while True:
  if tokens > 0:
    post = queue.pop()
    norm = normalize(post.text)
    tokens -= 1
    response = classifyML(norm)    # REST microservice
    saveToDB(post.id, response.sentiment, response.keywords)
  else:
    sleep(bucket.refillInterval)

Result. A self-healing, real-time intelligence platform that turned chaotic social chatter into structured, actionable insight — empowering analysts to see narrative shifts before they trended nationally.

• Goal. Enable live auctions with millisecond bid propagation and “anti-sniping” fairness rules directly within WordPress.
• Challenge. Deliver real-time concurrency and data integrity in an environment never intended for persistent sockets or event streams.
• Outcome. Production-grade plugin powering hundreds of simultaneous auctions, with built-in license verification, payment flows, and admin lifecycle tools.

Architecture (high-level).

React.js (Bid UI) ──▶ REST / WebSocket ─▶ Socket.IO Server (Node.js)
          │                              │
          │                              ├─ Auction Engine (state + TTL holds)
          │                              ├─ Payment Gateway (Stripe / PayPal)
          │                              └─ License API (Auth & Usage Limits)
          │
WordPress (PHP Plugin) ──▶ REST (bids, users, listings, logs)
          │
          └─▶ MySQL (persistent store) + Redis (live cache)
  

Key Engineering Highlights

• Socket Integration. Custom Node.js microservice bridged to WP REST routes, streaming live bids, price updates, and countdowns to React components in real time.
• Fair Bidding Logic. “Soft-close” anti-sniping extension; server-authoritative timestamps; idempotent bid submissions to prevent race duplication.
• Plugin Design. Modular CPT-based architecture with isolated namespaces, action hooks, and admin panels for auctions, users, and reports.
• Performance. Indexed lookups on auction_id / status / ends_at, cached leaderboards, batched event writes; sub-second bid propagation verified under load.
• Security. JWT-secured socket channels; license handshake with nonce + signature validation against dedicated license server.

License Server

• Purpose. Validate plugin activation, control environment count, and push version/patch updates.
• Stack. Node.js REST API + MySQL + Redis cache for validation tokens.
• Flow. On activation, plugin sends signed payload; server verifies, issues time-bound JWT, and logs fingerprint (domain + key + version).
• Result. Prevented piracy, enabled usage analytics, and provided remote deactivation capabilities for client compliance.

Sample Bid Flow

1. User places bid → React sends {auction_id, amount} via Socket.IO
2. Node.js validates hold + balance + TTL window
3. On success: broadcast "accepted" to all clients
4. WP REST endpoint persists winning bid, triggers payment intent
5. WebSocket emits "new_leader" and refreshed countdown

Result. Auctionify transformed static WordPress commerce into a pulse-driven, real-time experience — merging PHP reliability with Node.js immediacy. The hybrid architecture proved WordPress can host high-frequency transactional systems when engineered with discipline and precision.