Data Engineer – Python & ML Evaluation

We are looking for an experienced, hands-on Data Engineer to join a technology company developing advanced Data and AI-driven solutions.
This role combines Python development, data processing, code execution and validation, experiment evaluation, and automation of performance metrics.
The ideal candidate is a strong problem-solver with excellent technical skills, a proactive mindset, and the ability to quickly understand, troubleshoot, and adapt existing code.

Key Responsibilities
Develop and maintain data processing, analysis, and validation workflows using Python.
Execute, debug, and adapt code developed by research and engineering teams.
Validate outputs, identify issues, and ensure data accuracy and consistency.
Develop automated processes for collecting, calculating, and analyzing performance metrics.
Build and maintain evaluation workflows to assess and compare models and algorithms.
Collaborate closely with Data Science, Research, and Software Engineering teams.
דרישות התפקיד:
Requirements

Must-Have:
Bachelor's degree in Computer Science, Software Engineering, or a related field.
5+ years of experience as a Data Engineer or in a similar data-focused engineering role.
Strong hands-on Python development experience.
Proven experience with Pandas, NumPy, and SciPy.
Ability to understand, execute, troubleshoot, and debug existing code written by others.
Strong analytical and problem-solving skills, with the ability to learn quickly and adapt to changing technical challenges.
Independent, proactive, and hands-on approach.

Significant Advantages:
Experience with experiment tracking and evaluation platforms such as Weights & Biases (W&B) or MLflow.
Experience developing automated evaluation pipelines and performance metrics.
Experience working with Data Science, Machine Learning, or research teams.
Familiarity with model evaluation, validation, and performance benchmarking.

Who We're Looking For?
A technically strong, hands-on engineer who enjoys solving complex challenges, working with existing codebases, and building reliable, automated evaluation processes.

The role requires adaptability, attention to detail, and the ability to turn experimental code into reproducible workflows with validated outputs and measurable results.