This guide provides a curated set of technical and behavioral questions, along with sample answer tips, to help you evaluate Python Developers candidates consistently and fairly.
Python topped GitHub’s Octoverse in 2024 and holds the #1 spot on the April 2025 TIOBE index. The language keeps evolving—Python 3.12 delivers ~5 % speed gains and Python 3.13 will trial an optional GIL.
A Python Developer builds and maintains server-side applications, automates workflows and data pipelines, and ensures code quality through testing and CI/CD. They integrate front-end interfaces with back-end logic, optimize performance and scalability, and collaborate in Agile teams to deliver secure, maintainable software—often deploying to Docker/Kubernetes and cloud platforms.
Python Developers usually fall into one of these roles:
The typical responsibilities of a Python Developer are:
When it comes to Python software developer skills, these are the ones to look out for:
Look for mentions of pattern matching (match/case), faster CPython (specialised bytecode, with +25% performance), exception groups (PEP 654) and the new typing features. Python 3.12 also has continued support and enhanced security features, better error messages, and more expressive f-strings.
List comprehension creates new lists by applying an expression to each item in an existing iterable. Candidates should show both syntax and benefits over loops: performance and readability.
Expect understanding of reference counting, cyclic‑GC, and how __del__ may complicate garbage collection and should be used as a last resort.
A virtual environment is a self-contained Python directory that isolates projects from other projects outside of it. You use it to prevent conflict between projects. Candidates should reference venv or virtualenv, dependency isolation, reproductibility, consistency, simplified collaboration, and security best practices.
Args parameters collect positional arguments into a tuple, and kwargs collect keyword arguments into a dictionary. Look for positional vs keyword argument unpacking and examples.
Asyncio is a Python library that allows concurrent code to be more efficient. async defines an asynchronous task and await makes a task wait until the async task is done without blocking the rest of the code. Expect from your candidates some kind of coverage of cooperative multitasking, non‑blocking IO, and comparison to concurrent.futures.
Assess their understanding of match/case syntax, guards, and common pitfalls.
They should mention computed default values, async ORM, and Python 3.12 support.
FastAPI leverages Python type hints to automatically validate request data. It’s used to create high-performance APIs. Good answers cite Pydantic model parsing and automatic OpenAPI generation.
typing.Annotated is a type hint that allows you to attach metadata to existing types. The candidate should know metadata annotations for static type checkers and runtime frameworks like FastAPI (which uses Annotated for data validation).
Listen when they’re talking about pip-audit, hash‑pinning, PyPI attestations, and SBOMs. To defend against typo-squatting attacks, they may use a lock file as well. A good lock file has version pins, hashes, and full dependency graphs (through pip-tools).
dataclasses are used when you prefer simple data structures and reduce boilerplate code. Your candidate will look for reduction in boilerplate, immutability with frozen=True, and integration with typing.
PEP 703 makes Global Interpreter Lock (GIL) optional in CPython, allowing it to be disabled. This enables Python code to run without the GIL, potentially improving performance on multi-core CPUs and in multi-threaded applications. Expect your candidates to make a full explanation of multi‑core performance, C‑extensions impact, and backward compatibility strategies.
Strong candidates shall outline Depends, background queue, async DB drivers. They will likely follow these steps: 1. setting up a new project and dependences, 2. create a main application file and define the FastAPI instance, 3. implement dependency injection through depends, 4. use FastAPI to handle background tasks when a response is sent, 5. run the application and test it.
Should compare to asyncio.run() and manual loop handling.
This answer is simple enough: by identifying bottlenecks and applying strategies. You have to pay attention to how they would do it. Expect mention of profiling (through cProfile module) vectorization, numba, polars, memory layout, and the pandas 3.0 engine. A candidate which recalls choosing appropriate data types to minimize memory usage is worthy of their weight in gold.
Candidate should show proficiency with __enter__/__exit__ or contextlib decorator.
Look for GIL discussion, ProcessPoolExecutor, structured concurrency, and async advantages. Asyncio is better in IO-bound tasks, providing the best performance with minimal overhead. Multithreading improves performance over single-threaded execution, however it’s less efficient than asyncio. Multiprocessing is not ideal for IO-bound tasks due to higher overhead and doesn’t provide that much performance.