Data Engineer / ML Ops

• Design, develop, and maintain data transformation pipelines using certified transformation tools
• Implement and support ML Ops pipelines for model deployment, monitoring, and lifecycle management
• Work with SAS DI Studio for data integration and transformation activities
• Develop data processing and automation components using Python
• Ensure data quality, reliability, performance, and scalability of data pipelines
• Collaborate with data scientists, analysts, architects, and platform teams
• Support CI/CD practices for data and ML workloads
• Monitor and troubleshoot data pipelines and ML models in production
• Contribute to platform modernization and continuous improvement initiatives

• Intermediate to Senior experience in Data Engineering and/or ML Ops
• Certification in a data transformation tool (mandatory)
• Hands-on knowledge of SAS DI Studio
• Strong programming skills in Python
• Solid understanding of data modeling, ETL/ELT, and pipeline orchestration
• Experience with ML lifecycle management (deployment, monitoring, versioning)

Language Requirements

• At least one of the following languages:

• Dutch (NL)
• French (FR)
• English (EN)

• Professional working proficiency in the selected language

Nice to Have

• Experience with cloud-based data platforms
• Familiarity with containerization and orchestration (Docker, Kubernetes)
• Experience in regulated environments (banking, insurance, public sector)
• Knowledge of DevOps or DataOps practices