Data Engineer
Summary
Build and maintain ML pipelines for renewable energy forecasting using Python, AWS, and MLOps tools like Databricks and MLFlow.
Introduction & Summary:
We are looking for a passionate Python developer to join our team and contribute to the creation of Machine Learning models to forecast the energy production of our renewable assets (wind farms, photovoltaic plants). The ideal profile is a data engineer who understands the challenges of MLOps, is enthusiastic about Python development, and is capable of collaborating with Quants and Data Scientists to solve problems related to the Machine Learning model lifecycle.
Main Responsibilities:
As a Data Engineer, you will be responsible for developing and maintaining robust machine learning pipelines. Your core duties will include:
- Designing, implementing, and optimizing MLOps processes.
- Collaborating with data scientists on feature engineering and model training.
- Building and maintaining data pipelines for model training and evaluation.
- Ensuring the reliability and scalability of machine learning systems.
- Monitoring deployed models to ensure they meet performance standards.
Key Requirements:
- Advanced proficiency in Python.
- Experience with AWS ecosystem (S3, Step Functions, Athena).
- Knowledge of MLOps principles and practices.
- Familiarity with data engineering and ETL processes.
- Experience with Databricks, Feature Stores, and Model Registries (MLFlow).
Nice to Have:
- Experience with containerization technologies (Docker, Kubernetes).
- Knowledge of data visualization tools.
- Familiarity with Agile methodologies.
Other Details:
This position is remote and requires flexible working hours. Ideal candidates will be comfortable working in a dynamic and collaborative environment focused on renewable energy solutions.