Python Developer resume example
Python is used for web back ends, data pipelines, automation and machine learning, so say which kind of Python work you do. Then show the systems you built, their scale and what they made faster, cheaper or more reliable.
This is a fictional, illustrative example -- not a real person or a promised result. Replace every detail with your own.
Full example resume
Sample content, for illustration -- not a real person.
Python developer with 5 years of experience building APIs and data pipelines with FastAPI, Django and PostgreSQL. Rebuilt a nightly ETL job that dropped from 6 hours to 40 minutes.
- •Built a FastAPI service serving 3M requests a day for the customer analytics product at 90ms p95.
- •Rewrote the nightly ETL with async I/O and bulk inserts, cutting runtime from 6 hours to 40 minutes.
- •Moved background work to Celery, removing timeouts that affected 4% of user uploads.
- •Raised pytest coverage from 45% to 85% and added type checks to CI.
- •Built Django admin tools used daily by a 20-person operations team.
- •Wrote scripts that automated invoice reconciliation, saving 12 hours a week.
- •Fixed 60+ bugs reported by customers, with a 2-day average turnaround.
Summary: weak vs. improved
Python developer who writes clean, efficient code.
Python developer building APIs and data pipelines with FastAPI and PostgreSQL. Cut a nightly ETL from 6 hours to 40 minutes.
Every developer says "clean code". The type of Python work and one performance result are specific and memorable.
Skills to include
- ✓Python 3 (typing, asyncio)
- ✓Django, FastAPI or Flask
- ✓PostgreSQL and SQLAlchemy
- ✓Celery, queues and background jobs
- ✓pytest and CI
- ✓Docker and a cloud platform
Only list skills you actually have -- the AI review in the editor will flag a skill that doesn't appear anywhere else in your resume.
Bullet examples: weak vs. improved
Developed APIs in Python.
Built a FastAPI service serving 3M requests a day at 90ms p95.
Framework, traffic and latency show production-level work.
Optimised data processing jobs.
Rewrote the nightly ETL with async I/O and bulk inserts, cutting runtime from 6 hours to 40 minutes.
Techniques plus a before/after make the optimisation believable.
Wrote automation scripts.
Wrote scripts that automated invoice reconciliation, saving 12 hours a week.
Hours saved turns a script into business value.
How this changes by experience level
Show one or two deployed projects and any automation that saved real time.
Built a Flask app that tracks club memberships for 400 students, deployed on Render.
Show services and pipelines with scale and performance results, like the full example above.
See the full example above.
Show architecture, code standards and mentoring.
Set Python standards (typing, linting, review guides) for 15 engineers, halving review back-and-forth.
Section order
- Contact and links (GitHub)
- Short summary
- Skills
- Experience
- Projects or open source
- Education
Common mistakes to avoid
- !Not saying whether you do web, data or automation work
- !Listing every library you have imported once
- !No numbers on scale or performance
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