Scaling a Gig Economy Platform: Lessons from job.rocks

As CTO of job.rocks, I led the technical development of a recruitment platform that grew from a simple MVP to serving thousands of users across Europe. Here are the key technical decisions and lessons learned.

The Initial Architecture

We started with a straightforward stack:

• Frontend: Next.js for SEO and performance
• Backend: Node.js with Express
• Database: PostgreSQL
• Hosting: Single VPS

This worked perfectly for our first 100 users, but we quickly hit scaling challenges.

Challenge 1: Real-time Job Matching

Matching gig workers with jobs in real-time was computationally expensive. Our solution:

Intelligent Caching with Redis

const matchingCache = new Redis({
  host: process.env.REDIS_HOST,
  keyPrefix: 'matches:',
  ttl: 300 // 5 minutes
});

async function findMatches(worker) {
  const cacheKey = `${worker.id}:${worker.skillsHash}`;
  const cached = await matchingCache.get(cacheKey);
  
  if (cached) return JSON.parse(cached);
  
  const matches = await complexMatchingAlgorithm(worker);
  await matchingCache.set(cacheKey, JSON.stringify(matches));
  
  return matches;
}

Result: 90% reduction in matching time.

Challenge 2: Multi-country Compliance

Operating across Europe meant different labor laws, tax requirements, and payment systems.

Solution: Modular Architecture

// Country-specific modules
const countryModules = {
  DE: require('./countries/germany'),
  FR: require('./countries/france'),
  IT: require('./countries/italy')
};

class GigContract {
  constructor(country, worker, employer) {
    this.rules = countryModules[country];
    this.validateContract();
  }
}

Challenge 3: Payment Processing at Scale

Handling payments for thousands of gigs required:

• Stripe Connect for marketplace payments
• Webhook resilience with exponential backoff
• Idempotency keys for every transaction

Database Optimization Journey

Phase 1: Query Optimization

• Added proper indexes
• Implemented database views for complex queries
• Result: 70% faster response times

Phase 2: Read Replicas

• Separated read and write operations
• Geographic distribution for EU users
• Result: 99.9% uptime

Phase 3: Sharding Strategy

-- Sharding by employer_id
CREATE TABLE jobs_shard_1 PARTITION OF jobs
  FOR VALUES WITH (modulus 4, remainder 0);

Monitoring and Observability

What you can't measure, you can't improve:

• Sentry for error tracking
• Grafana + Prometheus for metrics
• Custom dashboards for business KPIs

Key Technical Decisions That Paid Off

1. Choosing PostgreSQL: JSONB columns gave us NoSQL flexibility with SQL reliability
2. Next.js from Day 1: SEO was crucial for organic growth
3. Microservices where it matters: Kept core monolithic, extracted heavy processes
4. Feature flags: Allowed gradual rollouts and A/B testing

Mistakes We Made (And Learned From)

1. Over-engineering early: We built for 1M users when we had 100
2. Ignoring mobile: 60% of gig workers use mobile exclusively
3. Not investing in testing: Technical debt accumulated quickly

The Results

• 10,000+ job matches
• 99.9% uptime
• <200ms average response time
• Successfully processed €5M+ in transactions

Looking Forward

The gig economy is evolving rapidly. Key areas I'm watching:

• AI-powered skill matching
• Blockchain for work verification
• Real-time collaboration tools

Building job.rocks taught me that scaling isn't just about technology—it's about making the right decisions at the right time.

Interested in the technical details? Check out my portfolio or get in touch.