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| # E-Commerce Platform Project | |
| ## Project Overview | |
| **Project Name**: Aurora Commerce Platform | |
| **Duration**: 18 months | |
| **Team Size**: 12 developers | |
| **Status**: Production | |
| ## Technology Stack | |
| - **Backend**: Python (Django), PostgreSQL, Redis | |
| - **Frontend**: React, TypeScript, Next.js | |
| - **Infrastructure**: Docker, Kubernetes, AWS (EKS, RDS, ElastiCache) | |
| - **CI/CD**: GitHub Actions, ArgoCD | |
| - **Monitoring**: Prometheus, Grafana, ELK Stack | |
| ## Architecture | |
| - Microservices architecture with 8 core services | |
| - Event-driven communication using Apache Kafka | |
| - API Gateway with rate limiting and authentication | |
| - CDN for static assets (AWS CloudFront) | |
| - Multi-region deployment for high availability | |
| ## Key Features Implemented | |
| 1. **User Management Service** | |
| - JWT-based authentication | |
| - Role-based access control (RBAC) | |
| - OAuth integration (Google, Facebook) | |
| 2. **Product Catalog Service** | |
| - Elasticsearch for product search | |
| - Image processing pipeline | |
| - Inventory management with real-time updates | |
| 3. **Order Processing Service** | |
| - Payment gateway integration (Stripe, PayPal) | |
| - Order state management | |
| - Fraud detection using ML models | |
| 4. **Recommendation Engine** | |
| - Collaborative filtering algorithms | |
| - Real-time recommendation API | |
| - A/B testing framework for recommendations | |
| ## Skills Required for Different Roles | |
| ### Backend Developer | |
| - **Essential**: Python, Django, PostgreSQL, REST APIs | |
| - **Intermediate**: Docker, Redis, Kafka, AWS services | |
| - **Advanced**: Microservices patterns, Event sourcing, Performance optimization | |
| ### Frontend Developer | |
| - **Essential**: React, TypeScript, HTML/CSS, JavaScript | |
| - **Intermediate**: Next.js, State management (Redux), API integration | |
| - **Advanced**: Performance optimization, SSR, Micro-frontends | |
| ### DevOps Engineer | |
| - **Essential**: Docker, Kubernetes, AWS, CI/CD pipelines | |
| - **Intermediate**: Infrastructure as Code (Terraform), Monitoring | |
| - **Advanced**: Service mesh, Security hardening, Cost optimization | |
| ### Data Engineer | |
| - **Essential**: Python, SQL, Kafka, Elasticsearch | |
| - **Intermediate**: ETL pipelines, Data warehousing, Apache Spark | |
| - **Advanced**: ML pipelines, Real-time analytics, Data governance | |
| ## Learning Paths Based on Project Needs | |
| ### New Backend Developer Onboarding (4 weeks) | |
| 1. **Week 1**: Python fundamentals, Django basics, PostgreSQL | |
| 2. **Week 2**: REST API development, Authentication systems | |
| 3. **Week 3**: Microservices concepts, Docker containerization | |
| 4. **Week 4**: AWS services integration, Testing strategies | |
| ### Frontend Developer Growth (6 weeks) | |
| 1. **Week 1-2**: React advanced patterns, TypeScript mastery | |
| 2. **Week 3-4**: Next.js SSR, Performance optimization | |
| 3. **Week 5-6**: State management, Testing, Deployment | |
| ### DevOps Transition Path (8 weeks) | |
| 1. **Week 1-2**: Containerization (Docker), Basic Kubernetes | |
| 2. **Week 3-4**: AWS services, Infrastructure as Code | |
| 3. **Week 5-6**: CI/CD pipelines, Monitoring setup | |
| 4. **Week 7-8**: Security, Performance tuning, Incident response | |
| ## Common Challenges and Solutions | |
| - **Scalability**: Horizontal scaling with Kubernetes HPA | |
| - **Data Consistency**: Event sourcing and CQRS patterns | |
| - **Performance**: Caching strategies, Database optimization | |
| - **Security**: OAuth 2.0, API rate limiting, Input validation | |
| ## Current Open Positions | |
| - Senior Backend Developer (Python/Django) | |
| - React Developer (TypeScript focus) | |
| - DevOps Engineer (Kubernetes/AWS) | |
| - Data Engineer (ML/Analytics focus) | |