Hire Python data talent who can ship

Python is the language most data teams use when SQL is not enough. A Python data hire writes the ingestion job, the feature table, the notebook that became a scheduled job, or the service that scores a model.

The work usually sits in three places:

• Pipelines. Reading from APIs, files, and databases, and writing tables other people can trust.
• Analysis. Pandas, Polars, or SQL in a warehouse, with a result a colleague can rerun.
• Models and services. Taking a notebook into something that runs on a schedule or behind an API.

The title on the CV may say data engineer, analytics engineer, or data scientist. Post the work you need. Python is the stack, not a separate profession.

You probably need this hire when:

• A critical report depends on a script only one person can run.
• Analysts are blocked because the extraction still lives in a notebook.
• You are moving a model or a scoring job out of a laptop and into production.
• Your warehouse is fine, and the gap is the Python around it.

If the gap is the warehouse platform itself, start with an AWS data engineer or an Azure data engineer. If the gap is the model in the warehouse, look at Snowflake or an analytics engineer.

Code another person can rerun. Look for jobs that fail loudly, write the same result twice without doubling the rows, and live in git.

SQL and Python together. Strong people pick the tool for the job. They push heavy joins into the warehouse and keep Python for the work SQL does not do well.

A path from notebook to schedule. They can say what changed when a notebook became a job: dependencies, retries, and who gets told when it fails.

What to ask

• Walk through a Python job you owned that failed in production. What did you change?
• When do you keep a transform in Python, and when do you move it into the warehouse?
• How do you test a pipeline without waiting for tomorrow's data?

carbon.talent is a data and AI talent platform. Post a role, see who applied, and hire.

1. Post a role. Title, stack, seniority, and whether you want permanent, contract, or fractional. Set remote or on-site, the country, and the overlap hours.
2. See who applied. Open each profile. Review a real-world data experience test, a working-style profile, and a video interview when they are on the profile.
3. Hire. Interview, agree the terms with the person, and hire on the platform. ID checks are on Team and Scale.

Already found someone? Test a candidate you found, then decide.

Companies post this role from anywhere. You choose where the person is based and which hours they overlap with your team.

Remote, hybrid, and on-site use the same flow. Location is a setting on the role.