Fair, Fast, & Focused: A Python Technical Interview Framework

Hiring the right Python engineers starts with a clear, fair, and structured interview process. This framework is for hiring managers, recruiters, and engineering leads who want to consistently evaluate candidates’ real-world skills while minimising bias.

It includes everything you need to run practical, scalable technical interviews, from async coding challenges and take-home projects to scoring rubrics and project templates. 

Using this framework helps you spot true ability, understand candidates’ problem-solving approach, and make objective hiring decisions without wasting engineering time.

Interview Question Bank

Here’s a ready-made set of questions to use when interviewing Python engineers. They’re designed to give you a fair read on a candidate’s fundamentals, problem-solving, and communication style. The questions are grouped by level, so you can easily match the difficulty to the role.

1. Junior-Level

2. Explain the difference between a list and a tuple. When would you use each?

3. What does the __init__ method do in Python?

4. How would you handle exceptions in Python? Provide an example.

5. Write a function to reverse a string.

6. What’s the difference between is and ==?

2. Mid-Level

1. Implement a simple class in Python with private and public attributes.

2. How does Python’s memory management work?

3. What are list comprehensions and why are they useful?

4. Write a decorator that logs function execution time.

5. Explain how Python handles multithreading and multiprocessing.

3. Senior-Level

1. Design a simplified caching system using a Python class.

2. How would you handle 10,000 concurrent requests in a Python web app?

3. What are the tradeoffs between async and threading in Python?

4. Write a class-based context manager.

5. Explain how garbage collection works in Python.

Scoring Rubrics

To keep evaluations consistent and fair, use this rubric for each question or challenge. It focuses on four key areas that matter in real-world engineering: technical accuracy, code quality, problem solving, and communication.

Candidates can score up to 12 points total.

Async Coding Challenges

These exercises let you see how candidates think about async Python in practice. They’re short, real-world exercises designed to reveal problem-solving, coding style, and understanding of concurrency.

Challenge 1: Async Web Scraper

Write an async function that takes a list of URLs and fetches them concurrently using aiohttp. Parse the HTML titles and return them as a dictionary. 

Evaluate for correct use of async/await, error handling with try/except, and concurrency with asyncio.gather.

Challenge 2: Async API Caller with Rate Limiting

Call a paginated API (e.g., a mock GitHub endpoint) 100 times but no more than 10 calls per second. 

Check for proper use of asyncio.Semaphore or asyncio.sleep, throttle logic, and correct pagination handling.

Take-Home Project Templates

Give candidates a project that mirrors real-world work. This shows how they approach system design, code quality, and documentation in a setting similar to day-to-day engineering.

Project: Mini Blogging Platform (Backend Only)

Requirements:

• REST API with endpoints for creating posts, users, and comments

• Auth with JWT

• CRUD operations

• Unit tests and API documentation

Tech Stack: FastAPI or Flask

Evaluation Criteria:

• Code structure and readability

• API design

• Test coverage

• Error handling and validation

Live Coding Templates

These exercises let you see how candidates approach problems, structure code, and communicate their reasoning in real time.

Template 1: FizzBuzz with a Twist

Write a variation of FizzBuzz that takes a list of rules and applies them dynamically.

Template 2: File Parser

Parse a CSV and return aggregate values from one column (e.g., average, max, count of unique values).

Both exercises are 15-30 minutes long, designed to prompt discussion and explore candidates’ thought processes, with optional extensions to test deeper understanding.

Framework Deep-Dives

See how candidates handle real-world frameworks and evaluate coding, architecture, and problem-solving skills.

FastAPI Deep-Dive

Build a small API that uses dependency injection and background tasks. Make async DB calls to a database using SQLModel or asyncpg, and evaluate your API for REST principles, error handling, and test coverage.

Django Deep-Dive

Implement a model-view-template flow. Use the Django admin to manage data and create a simple form for submitting entries.

Async/Await Evaluation

Use these questions and tasks to gauge a candidate’s async Python skills. Ask about await, the event loop, async vs threading, and have them spot anti-patterns or refactoring sync functions to async.

Topics & Questions:

1. What does await do in Python?

2. Explain the event loop.

3. What are the benefits of async vs threading?

4. How do you cancel an async task?

5. Give an example where async I/O would dramatically outperform sync code.

Code Review Tasks:

• Refactoring sync functions to async

• Spotting async anti-patterns (e.g., blocking calls)

Code Review Simulation

See how candidates approach reviewing others’ code. 

Give them a small PR (100–150 LOC) with obvious bugs, edge-case gaps, smells (e.g., no input validation, tight coupling), confusing naming, or poor abstractions. 

Ask them to identify 3-5 issues they’d flag, write a short summary like they would in GitHub, and optionally submit a refactored version.

Following this framework helps you run fair, structured, and efficient Python interviews, giving you confidence in hiring candidates who can write clean, maintainable code, tackle real-world problems, and integrate smoothly with your team’s workflow.

Looking to improve your Python interview process? 

If you’d like tailored advice or want to learn more about attracting the right candidates, reach out to Joshua Smith, he’ll be happy to help.