Engineering
Case Studies

Deep technical breakdowns of production systems — architecture decisions, hard problems, and lessons earned in production.

Cybersecurity

RiskProfiler Architecture

Serverless Cybersecurity Platform

AWS LambdaDynamoDBS3PythonServerless

The Problem

Organizations struggle to continuously monitor their attack surface across cloud environments. Traditional solutions are expensive, difficult to scale, and require significant infrastructure management overhead.

Architecture

  • AWS Lambda functions for compute isolation and automatic scaling
  • API Gateway for RESTful endpoints with built-in authentication
  • DynamoDB for fast, scalable NoSQL storage with single-digit millisecond latency
  • SQS FIFO queues for ordered message processing and per-customer rate limiting
  • S3 for storing scan results and threat intelligence data
  • CloudWatch for monitoring, logging, and alerting across all components

Technical Challenges

  • Handling bursty traffic patterns from scheduled scans across multiple customers
  • Implementing effective rate limiting to avoid overwhelming third-party APIs (Shodan, VirusTotal)
  • Designing DynamoDB schemas for efficient querying without table scans
  • Managing Lambda cold starts for time-sensitive vulnerability assessments
  • Correlating events across distributed Lambda invocations without shared state

Key Design Decisions

  • Chose DynamoDB over RDS for predictable performance at scale and operational simplicity
  • Used SQS FIFO for guaranteed ordering in vulnerability processing pipeline
  • Implemented Lambda layers for shared code to reduce package size and cold start time
  • Created separate Lambda functions per scan type to optimize memory allocation
  • Used Step Functions for complex multi-step vulnerability assessment workflows

Lessons Learned

  • Serverless is excellent for unpredictable workloads but requires different thinking about state
  • DynamoDB schema design is critical — get it right early or face expensive migrations
  • Monitoring and observability are even more important in distributed serverless architectures
  • Cold starts matter: optimize package size and use provisioned concurrency for latency-sensitive paths
  • Event-driven architecture requires careful error handling and comprehensive retry logic
Cybersecurity

CloudFrontier

Cloud Attack Surface Monitoring

PythonShodanVirusTotalDockerPostgreSQLCeleryRedisFlask

The Problem

Security teams need visibility into their internet-facing assets but lack tools to continuously discover and monitor exposures across multiple cloud providers and on-premise infrastructure.

Architecture

  • Python-based scanning engine with Shodan and VirusTotal integrations
  • PostgreSQL for relational data storage and historical asset tracking
  • Docker containers for consistent, isolated scanning environments
  • Celery task queue with Redis for distributed job processing
  • Flask REST API for programmatic access and webhook integrations
  • Plugin architecture for extensible data source support

Technical Challenges

  • Rate limiting across multiple third-party APIs with different quotas and pricing
  • Deduplicating assets discovered through multiple overlapping data sources
  • Handling false positives in vulnerability detection without flooding teams
  • Scaling scan operations across large IP ranges without blocking the queue
  • Managing credentials and API keys securely across deployment environments

Key Design Decisions

  • Used Celery for task distribution to handle long-running scans asynchronously
  • Implemented Redis caching layer to minimize redundant API calls and costs
  • Created plugin architecture for community-driven extension of data sources
  • Used Docker for consistent scanning environment across development and production
  • Implemented webhook notifications for real-time alerting instead of polling

Lessons Learned

  • Plugin architecture enables community contributions and rapid feature development
  • Rate limiting is not just about respecting API quotas — it's about being a good citizen
  • False positive management is as important as detection accuracy for adoption
  • Real-time notifications are far more valuable than comprehensive batch reports
  • Open-sourcing early attracts contributors who improve the product faster than solo development
Fintech

WageFi Microservices

Payroll Infrastructure System

Node.jsPostgreSQLRabbitMQRedisStripeDocker

The Problem

Traditional payroll systems are monolithic, difficult to customize, and struggle to integrate with modern payment processors. Teams need a flexible, auditable payroll platform that can scale independently.

Architecture

  • Microservices architecture with separate Node.js services per domain
  • PostgreSQL for transactional payroll data with ACID guarantees
  • RabbitMQ for reliable inter-service communication
  • Redis for session management, caching, and distributed locks
  • Stripe for payment gateway processing with idempotency keys
  • API Gateway for service orchestration and unified authentication

Technical Challenges

  • Maintaining data consistency across services without distributed transactions
  • Handling payment failures with robust retry logic and idempotency guarantees
  • Managing service dependencies while avoiding cascading failures
  • Ensuring exactly-once payment processing at the gateway level
  • Coordinating multi-service deployments with zero downtime

Key Design Decisions

  • Implemented saga pattern for distributed transaction coordination
  • Used idempotency keys throughout the payment flow for safe retries
  • Created circuit breakers to prevent cascading failures across services
  • Used event sourcing for full audit trail and regulatory compliance
  • API Gateway for centralized authentication, rate limiting, and observability

Lessons Learned

  • Microservices add real complexity — ensure the scaling benefits justify the costs
  • Event-driven communication reduces coupling but makes debugging significantly harder
  • Idempotency is essential in distributed financial systems, not optional
  • Invest heavily in observability across services before you need to debug production
  • Start with a modular monolith; extract services only when you have clear domain boundaries

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