▸ Business Automation Engineer

I replace spreadsheets with scalable software.

From Excel and Google Sheets to AI-powered React + Express + PostgreSQL platforms.

Excel→ SAP→ Google Sheets→ AppSheet→ React→ Express→ PostgreSQL

See case studies → How I work My path

What problems I solve

Your company has

  • ✗ Excel everywhere
  • ✗ Google Sheets with 50k rows
  • ✗ AppSheet becoming too slow
  • ✗ Shopify processes done manually
  • ✗ Employees copying data between systems
  • ✗ Several disconnected tools

I can build

  • ✓ Multi-tenant CRM
  • ✓ Shopify integrations
  • ✓ Internal business tools
  • ✓ AI-assisted workflows
  • ✓ React frontends + Express APIs
  • ✓ PostgreSQL databases

Typical projects

Migration

  1. Excel↓
  2. Google Sheets↓
  3. AppSheet↓
  4. React↓
  5. Express↓
  6. PostgreSQL

Automation

  1. Manual process↓
  2. API integration↓
  3. Background jobs↓
  4. Notifications↓
  5. Analytics

Commerce

  1. Shopify↓
  2. Inventory↓
  3. Orders↓
  4. CRM↓
  5. Accounting

What's your pain?

Pick the one that sounds like your situation — I'll show the closest case I've shipped.

Pick the one that sounds like your situation — I'll show the closest case I've shipped, with the measured result.

Case studies

★ Featured

Migrating an AppSheet CRM to Express + PostgreSQL

Replaced a no-code AppSheet CRM hitting its limits with a typed Express + PostgreSQL backend and a React front-end — without losing a single record.

Result Cut manual data-sync from ~6 hrs/week to near-zero; eliminated the daily row-count reconciliation.

  • TypeScript
  • Express
  • PostgreSQL
  • React
  • Drizzle ORM
 ★ Featured

Building a Multi-tenant CRM

A single Postgres-backed CRM serving multiple isolated tenants — schema-per-tenant isolation, shared application code, and zero cross-tenant data leaks.

Result 5 tenants on one codebase; isolation guaranteed by an automated property test on every PR; per-tenant export in one command.

  • TypeScript
  • Express
  • PostgreSQL
  • React
  • Drizzle ORM

Uploading 150 MB Audio Files to S3 Without Killing the Server

A direct-to-S3 multipart upload pipeline for large audio — presigned URLs, client-driven parts, and a worker that finalizes assembly. Zero bytes touch the app server.

Result Server memory flat (~120 MB) regardless of file size; ~300 MB uploads in under a minute on a 1-core box; resumable after drops.

  • TypeScript
  • Express
  • React
  • S3/R2
  • AWS SDK v3

All case studies →

Latest writing

All posts →

Questions? Email me · GitHub · LinkedIn