Design and operate scalable data architectures (batch and real-time)
Productionize data pipelines and ML workflows (MLOps)
Deploy and monitor machine learning models in production
Ensure data quality, reliability and governance
Collaborate with Data Scientists, Architects and business teams
Challenge technical choices and contribute to best practices
Contribute to the continuous improvement of data platforms (performance, cost, security)
Occasionally mentor more junior team members

University degree or engineering diploma
Minimum 5 years of proven experience in a similar role
Comfortable working in demanding environments
Ability to design robust and scalable data architectures
Expertise in productionizing pipelines and MLOps
Analytical mindset, rigor and a strong focus on data quality
Technical leadership and ability to support and guide teams
Strong interest in Cloud and modern data technologies
Technical skills: Python, SQL, Spark, Airflow, Kafka, Docker, Kubernetes, Cloud (Azure, AWS or GCP), CI/CD, Git, ML frameworks (TensorFlow, PyTorch, Scikit-learn), Data Lake, Data Warehouse, MLOps

Senior Data Engineer

Key skills