Table of Contents
Many candidates claim Python expertise. Few understand production backend engineering. There's a vast difference between writing data science scripts and building APIs that serve millions of requests. Here's what to look for in a Python backend engineer.
Core Backend Python Skills
Non-negotiable:
- ✦ Type hints (PEP 484) for maintainable codebases
- ✦ Async/await understanding (not just syntax, but event loop)
- ✦ Context managers (with statements for resource management)
- ✦ Decorators for cross-cutting concerns (logging, auth, rate limiting)
- ✦ Generators for memory-efficient data processing
- ✦ Exception handling and logging best practices
Framework Expertise
Look for depth in at least one:
- ✦ FastAPI - modern, async, type-checked, automatic docs
- ✦ Django - full-featured, admin panel, ORM, migrations
- ✦ Flask - lightweight, extensible, good for microservices
- ✦ Understanding of trade-offs between frameworks
Database & Data Skills
Must have:
- ✦ SQL (joins, aggregations, indexing, query optimization)
- ✦ ORM knowledge (SQLAlchemy, Django ORM) and when to avoid them
- ✦ Connection pooling and transaction management
- ✦ Caching strategies (Redis, memcached)
- ✦ Background tasks (Celery, RQ, Dramatiq)
Production Engineering Skills
Often missing in resumes:
- ✦ Observability and distributed tracing
- ✦ Rate limiting
- ✦ API versioning
- ✦ Secrets management
- ✦ Containerization
- ✦ Horizontal scaling
Testing & Code Quality
Signs of seniority:
- ✦ Unit tests with pytest or unittest
- ✦ Integration tests for API endpoints
- ✦ Mocking external services
- ✦ Test coverage (not 100%, but strategic)
- ✦ CI/CD integration for automated testing
Python Backend Hiring Scorecard
Python Fundamentals
Weight:
20%
API Design
Weight:
20%
Databases
Weight:
20%
Testing
Weight:
15%
Infrastructure
Weight:
15%
Observability
Weight:
10%
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