Data Platform Engineer (Python)

Python data infrastructure

Develop production systems supporting AI data pipelines and model evaluation. The role combines full-stack tools, backend services, and systems engineering for large-scale data work.

• Design and optimize Python systems for training data and evaluation workflows.
• Build services for annotation, validation, and quality control.
• Improve reliability, safety, and performance in existing codebases.
• Diagnose unusual data behavior and system bottlenecks, then implement scalable fixes.
• Coordinate with data, research, and engineering colleagues.
• Participate in live design reviews covering architecture and implementation decisions.

Required experience

At least five years of professional production Python experience in data engineering is required. You need a full-stack background with strong systems programming skills.

Proficiency with orchestration tools for complex dependency graphs is essential, along with dataframe processing libraries and integrating cloud data warehouses through Python SDKs. Native or fluent English and clear spoken and written communication are required.

Useful background

Annotation, data quality pipelines, evaluation systems, AI or ML workflows, training infrastructure, benchmarking, distributed systems, and developer tools are relevant experience.

Terms

This fully remote hourly contract requires reliable availability for 20 to 40 hours per week. Scheduling is flexible, with distributed collaboration on substantial production systems and possible ongoing work or contract extension.