How to hire Python developers: define the stack first

"Hire a Python developer" tells a recruiter almost nothing. Python builds web backends, moves data through pipelines, and trains machine-learning models, and the engineer who's excellent at one of those is often no use for another. Before you set a budget or pick a channel, the work that pays off is deciding which Python job you actually mean.

"Python developer" is really three jobs

A web or backend engineer working in Django, FastAPI or Flask spends the day building services and APIs. A data engineer in Airflow and Spark builds the pipelines that feed everything else. A data scientist or machine-learning engineer in PyTorch and TensorFlow trains and ships models. They all write Python, they read the same job title, and they are not interchangeable. Ask for a generic "Python developer" and the strong people assume you don't know which one you need.

The stack decides which pool you're fishing in

This is where defining the stack earns its keep. More than 120,000 engineers on cord list Python, but they don't split evenly. Around 23,500 also tag machine-learning skills, about 10,800 tag data-engineering tools like Airflow or Spark, and roughly 9,000 tag a web framework like Django or FastAPI. So "Python developer" isn't one pool of 120,000. It's several pools of very different sizes, and the words in your spec decide which one you're actually searching.

The surprise is what the stack doesn't change

You'd expect those three to pay very differently. On cord's UK listings they mostly don't: Python roles in web, data engineering and machine learning all advertise a median of £60,000 to £80,000. The stack moves who you hire and how hard they are to find, not the number on the advert.

Where pay actually separates is seniority and role level, and it shows up clearest on the data side, where analyst, data scientist, data engineer and machine-learning engineer are four genuinely different markets. Budget against the level and the specialism, not the language.

The full UK data salary ladder sets out the advertised band for each of those four roles.

What to write down before you post

Three things, in plain words. The Python job itself: a web service, a data pipeline, or a model. The two or three tools that genuinely matter, Django or Airflow or PyTorch, rather than a wishlist of twenty. And the level, because that, not the word "Python", is what sets the number. That spec is the difference between a search that reaches the right engineers and an advert the right ones scroll past.

Where to find them once the stack is clear

A precise stack is only worth writing if you can act on it, filtering for the exact framework instead of posting and hoping. That's the point of sourcing directly: you search for the Django engineer or the PyTorch specialist and message them, rather than waiting for whoever happens to apply. Python roles filled that way on cord close in a median of 28 days, with no agency fee on top. Define the job, then go to the people who match it.

The bottom line

"Python developer" is a category, not a role, so hiring one starts with a decision rather than a job post: which Python job is this, and at what level. Get that down and the pool, the rate and the search all come into focus. Once you've named it, you can see how many engineers match the exact stack before you commit to a hire. Take a look at who fits on cord.

Hiring for a different stack? There's the same breakdown for Java, React and AI engineers.