How to Hire the Best Python Developers: 2025 Guide

Hard skills (the must-haves)

Strong Python developers should be comfortable writing production-ready code in Python 3. Most teams have long since moved away from Python 2. In fact, only about 6% of developers still use it today, with version 3 approaching near 100% adoption.

If your project requires legacy compatibility, make sure to call that out early, but in most cases, Python 3 is the default.

Here’s what else to prioritize:

• Core Python proficiency: They should understand data types, error handling, object-oriented programming, and standard libraries like datetime, json, and os. Look for clean, efficient code and a solid grasp of idiomatic Python.
• Framework experience: Django, Flask, and FastAPI are common in web and API development. The right fit depends on your tech stack, but familiarity with at least one is essential.
• REST API development: Most Python developers will be building or consuming APIs. Ask about their experience designing endpoints, handling authentication, and managing versioning.
• Testing: Unit testing and test-driven development aren’t optional for Python devs. Proficiency in PyTest and UnitTest shows they can catch bugs early and improve maintainability.
• Database fluency: Whether you’re using PostgreSQL or MongoDB, your developer should know how to interact with both SQL and NoSQL databases, write efficient queries, and avoid common performance pitfalls.
• CI/CD, Docker, and Microservices: If your infrastructure is containerized or service-oriented, your developer should know how to deploy safely and work within a distributed system.

Soft skills (equally important)

Python developers often work across teams, collaborating with product managers, data scientists, or other engineers. The right soft skills make that collaboration productive.

Look for:

• Clear communication: Strong developers don’t just write great code—they explain their decisions clearly. Whether they’re discussing architecture with a senior dev or scope with a non-technical PM, clarity matters.
• Ownership and accountability: Great candidates speak in terms of responsibility. They don’t just list tools but talk about outcomes too.
• Receptiveness to feedback: One of our recruiters recommends asking how they’ve handled feedback in the past. A good answer shows reflection and growth.
• Curiosity and problem-solving: Python is used in so many domains because it rewards creative thinking. Look for candidates who are eager to explore new tools, learn independently, and ask smart questions when things get complex.

Bonus skills (the differentiators)

Not every Python developer will have these, but when they do, it’s a strong signal of adaptability and long-term value.

• Cloud platform experience: Comfort with AWS, GCP, or Azure can make deployment and scaling smoother.
• Data modeling and optimization: If your app is database-heavy, someone who understands schema design and indexing can prevent performance issues and make scaling much easier down the line.
• Distributed systems experience: High-traffic platforms benefit from devs who understand how to build resilient, asynchronous systems.
• Python for ML, automation, or scripting: These aren’t must-haves for a backend dev, but they’re useful for growing teams that want flexibility or are involved in AI- and ML-specific projects.
• Security best practices: In fintech or enterprise software, devs need to think critically about input validation, authentication, and secure storage.

Choosing local, national, or global talent

Your first step is deciding whether to hire in-office, within the US, or expand your search internationally.

Each option has its trade-offs, and your ideal choice should depend on your goals, budget, and how your team works.

In-office hires:

• Best for: Companies that value direct collaboration and on-site teamwork
• Pros: Face-to-face interaction, easier onboarding, alignment with in-office culture
• Cons: Smaller talent pool, highest salary expectations, less flexibility

Remote US-based hires:

• Best for: Teams that want flexibility while staying within US time zones and systems
• Pros: Broader candidate reach than local-only, culturally aligned, fewer compliance concerns
• Cons: Still tied to US salary bands, where salary can reach high into six figures.

Global hiring:

• Best for: Companies open to high-quality talent at a lower cost
• Pros: Access to a much wider pool of developers, significant cost savings, increased diversity
• Cons: Requires more thoughtful vetting, and depending on region, possible time zone, or language challenges

If you’re hiring outside the US, Latin America often strikes the right balance. Developers in the region share overlapping work hours with US teams, have strong English skills, and come from a growing engineering culture with deep technical talent.

This geographical and cultural alignment reduces some of the biggest challenges in offshore development, which can make a big difference when building long-term engineering capacity.

That said, other regions like Eastern Europe (Poland, Romania) and South/Southeast Asia (India, Philippines, Vietnam) also offer strong Python expertise. These regions are worth considering if you’re comfortable with asynchronous workflows or already have support in those time zones. 

Choosing the right sourcing channel

Once you’ve decided where to search, the next step is figuring out how to find the right candidates. Here’s how common sourcing channels stack up for hiring Python developers:

If you’re building out a team or don’t have time to manually screen dozens of candidates, working with a specialized recruiter can be a smart move. 

This is especially true for international hiring, where partnering with a specialist recruitment company can help reduce hiring risk, speed up the process, and bring structure to what’s often a messy search. We’ll cover that approach a little more later on. 

In many cases, companies combine multiple channels: referrals to kick things off, a job board listing for reach, and a recruiting partner for speed or hard-to-fill roles. The best approach depends on your timeline, in-house capacity, and the complexity of the role.

Stage 1: Set clear expectations before you start sourcing

Stage 2: Screen for problem-solving and real-world fit

Stage 3: Make a competitive offer and move fast

“How would you structure a Python application that needs to scale as traffic grows?”

This question tests architectural thinking and real-world experience with scalable systems. Look for candidates who talk about modularity, dependency management, asynchronous processing (e.g., Celery), and tools like FastAPI or Django in production.

Strong answers include clear reasoning behind decisions, mention of load testing, and awareness of bottlenecks.

Red flags: These include vague talk about just “adding more servers” or no mention of code structure at all.

“Walk me through how you’ve used Python’s built-in tools to debug a tricky issue.”

Python offers powerful introspection and debugging capabilities. This question uncovers familiarity with tools like pdb, logging, traceback, or context managers.

You want someone who doesn’t reach for Stack Overflow immediately but can use the language’s strengths to investigate issues independently.

Avoid candidates who rely entirely on external libraries for basic debugging.

“How do you approach writing unit tests for Python code that integrates with external APIs or services?”

This probes their testing mindset. Do they use mocks effectively? Can they isolate business logic from I/O? Look for familiarity with unittest.mock, pytest, or dependency injection patterns.

Good candidates will explain how they write tests that are fast, meaningful, and maintainable.

Red flags: Saying they “usually skip testing” for integrations or only test via the live API.

“What’s a Python feature you once misused or misunderstood—and how did you fix it?”

This surfaces humility and technical growth. Python has many quirks, such as mutable default arguments, late binding in closures, or thread-safety issues with data structures.

Ideal answers show a developer who can reflect critically, self-correct, and explain their reasoning.

Red flags: Anyone who insists they’ve never run into issues with the language.

“Can you explain a situation where you used a Python design pattern to make your code more maintainable?”

This checks for both technical maturity and code quality standards. Listen for references to common Python patterns like factory functions, decorators, context managers, or even async task queues.

Good responses include real examples with context—what the pattern solved and how it improved readability or reuse.

Red flags: Vague answers like “I think I used one once” suggest inexperience with larger systems.

1. Prioritizing cost over quality

Hiring outside the US can reduce hiring costs, but focusing solely on budget often results in trade-offs that hurt your product long-term. Hiring the cheapest available developer can lead to messy code and performance issues.

What seems like a win in the short term often ends in rework, delays, or even a complete restart.

If you’re hiring offshore, the goal shouldn’t be to pay as little as possible. It should be to find skilled developers whose rates reflect their local market and not a lack of capability.

2. Filtering out candidates without formal degrees

One of the most common blind spots in hiring developers is rigid degree requirements. For example, according to our recruiters, many of the strongest engineers in Latin America started working before finishing college or never attended at all. Their real-world experience often outpaces those with more traditional credentials.

By excluding candidates without a CS degree, you risk missing out on developers who are already delivering production-grade code in high-performing teams. Instead of scanning for education history, look for clarity in problem-solving and ownership in past projects.

3. Mistaking tool familiarity for Python fluency

Not all Python developers use the same tools, and that’s not a red flag. Too many hiring managers judge candidates based on whether they’ve worked with a specific library or framework, like Flask vs. FastAPI, or Pandas vs. NumPy. But great Python developers can ramp up quickly when the core fundamentals are solid.

While Django, Flask, and FastAPI are the most commonly used frameworks, not every strong developer will have used your exact stack. That doesn’t mean they’re unqualified. In many cases, those who’ve worked with different but comparable tools bring fresh perspectives and adaptability.

Focusing too narrowly on one ecosystem can exclude candidates who are more than capable of contributing.

Instead of filtering for a specific toolset, ask how a candidate approaches unfamiliar technologies, what they look for when choosing a library, and how they stay up to date with Python’s ecosystem. 

4. Dragging out the hiring process

We’ve already touched on this, but it’s worth pointing out again: The market for Python developers is competitive, and strong candidates rarely stay available for long. According to our recruiters, long interview processes with too many steps or delays between stages are one of the most common reasons companies lose top talent.

If you’ve found someone who meets your criteria, move fast. Let candidates know what to expect, communicate timelines clearly, and minimize unnecessary steps. Otherwise, another company will move faster.

5. Underestimating the importance of collaboration and communication

Python developers rarely work in isolation. Whether they’re writing backend logic, integrating APIs, or supporting analysts, they need to collaborate effectively with teammates across technical and non-technical roles.

A technically brilliant Python developer who can’t explain their choices or receive feedback constructively can slow your team down. During interviews, watch how candidates communicate and not just how they code.