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CV template · Python Developer

Python Developer CV Template

Python Developers ship backends, automation and data pipelines, so your CV needs to prove which frameworks you actually use and what kind of load you've handled. This template helps you build a resume that passes ATS and hooks tech leads with concrete latency, RPS and pipeline numbers instead of generic 'Python expert' lines.

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What recruiters look for

Top signals on a Python Developer CV

  • Specific framework experience: Django, FastAPI or Flask in every bullet
  • Hands-on async/await work, not just textbook knowledge
  • pytest coverage treated as a default, not a bonus
  • Real ORM and PostgreSQL work, beyond raw psycopg calls
  • Celery or other queues for background jobs
  • Docker and basic cloud deploy familiarity
  • GitHub with working code, not tutorial TODO apps
Key skills

Skills to feature on a Python Developer CV

Hard skills
Python 3.10+Django, FastAPI, Flaskasyncio, async/awaitpytest, pytest-asyncioPostgreSQL, SQLAlchemyMongoDB, RedisCelery, RabbitMQDocker, docker-composeREST API, OpenAPIpandas, numpy for ETLCI/CD (GitHub Actions, GitLab CI)System design and architecture
Soft skills
Breaking down complex problemsMentoring juniors via code reviewClear communication with productOwnership of technical decisionsJustifying architecture trade-offs
Sample bullets

Ready-to-use lines for your CV

Copy these as starting points and swap in your own numbers.

  1. 01Designed a FastAPI backend for an 80k MAU SaaS, holding 110ms p95 latency at 800 RPS peak.
  2. 02Rewrote sync Django views to async with httpx, dropping heavy endpoint response time from 1.4s to 220ms.
  3. 03Raised pytest coverage on a legacy service from 38% to 86%, cutting regressions in half within a quarter.
  4. 04Moved heavy report generation to Celery with a Redis broker, eliminating timeout failures on the main API during peak hours.
  5. 05Optimised SQLAlchemy queries with eager loading and indexes, reducing PostgreSQL load by 55%.
  6. 06Built a pandas ETL for nightly processing of 12M records, cutting pipeline runtime from 4 hours to 35 minutes.
  7. 07Containerised 6 Python services in Docker and wired GitHub Actions, bringing deploy from merge to prod down to 9 minutes.
  8. 08Mentored 2 juniors over a year; both passed performance review and were promoted to middle.
  9. 09Integrated Stripe via webhooks with idempotent handlers, zero duplicate charges over 6 months in production.
Salary ranges

What Python Developer earn

2024-2025 estimates. Wide ranges by experience and seniority.

Market
Junior
Mid
Senior
Ukraine
$1,200-2,000 USD/mo
$2,300-4,000 USD/mo
$4,200-7,500 USD/mo
EU
2,800-4,200 EUR/mo
4,500-7,000 EUR/mo
7,200-11,000 EUR/mo
USA
$95,000-130,000 USD/yr
$135,000-185,000 USD/yr
$185,000-270,000 USD/yr
Interview prep

5 questions Python Developer candidates hear

  1. Q1Explain how the GIL works and whether you've actually hit it in production.
  2. Q2What's the difference between async, threading and multiprocessing in Python, and when do you reach for each?
  3. Q3Walk me through a FastAPI or Django service you built from scratch. How did you structure it and why?
  4. Q4How do you approach tuning a slow ORM query? What's your first move and which tools do you use?
  5. Q5Tell me about the worst production incident on a Python service and what you took away from it.
FAQ

Common questions about this CV

Django or FastAPI to start a career in Python?

Django has more batteries-included tooling and more product-company jobs. FastAPI shows up more in new services and ML-adjacent backends. For a junior either is fine; what matters is shipping one to production with tests and a deploy.

Do I need solid async at mid level?

Yes. Async calls to external APIs and background tasks are now the default. You don't need to write your own event loops, but you should be comfortable with asyncio, httpx and spotting when sync code blocks the process.

Can I build a Python career without ML or data science?

Absolutely. Most Python roles are backend, integrations, automation and platform work. ML is a separate track with its own math requirements; don't confuse the two paths.

How much code should my GitHub show?

Quality, not quantity. Two or three finished services with tests, a README and a live deploy answer the question better than 20 half-finished tutorial repos.

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