EU remote
AI Analytics Engineer Go to Market (f/m/div.)
About this role
Your contribution to something big: As a Data Engineer , you will build scalable data solutions that digitalize procurement processes and enable AI-driven initiatives. Working in an international team, you will turn business needs into reliable, high-quality data products. Build and operate scalable data solutions: Develop, maintain and continuously improve automated ETL pipelines, data products and data models that support digital procurement and AI use cases.
Turn business needs into data solutions: Translate customer and stakeholder requirements into robust data transformations and models that enable reporting, analytics and informed decision-making. Ensure data quality and reliability: Establish and continuously improve data integrity, security and quality by working closely with data producers and consumers. Enable data-driven decisions: Support ad-hoc analyses and develop solutions that can be documented, scaled and reused across the organization.
Drive reliable data operations: Apply DevOps principles to ensure the stability, monitoring and continuous improvement of automated data pipelines, including data testing, CI/CD and KPI tracking. Collaborate internationally: Work closely with project management, customers, IT teams and other stakeholders, balancing business needs with technical and organizational frameworks. Bring a service-oriented mindset: Act as a trusted partner for data consumers and stakeholders, focusing on practical solutions and measurable business value.
Explore the future of AI: Stay informed about the latest developments in Generative AI, including agentic AI, and identify opportunities to apply these technologies to real-world business challenges. What distinguishes you: Education : Master's degree in Computer Science or a related field, or equivalent professional experience. Experience 5+ years of professional experience in data engineering, data modeling, ETL processes and data warehouses/lakehouses.
Experience designing and operating automated data pipelines and scalable data solutions. Practical experience working with data and analytics platforms such as Databricks, Power BI or Tableau. Know-how: Strong proficiency in SQL; experience with Python is an advantage. Solid understanding of data modeling, ETL/ELT processes, data warehouses and lakehouses. Proven knowledge of DevOps principles, including data testing, CI/CD, monitoring, KPI definition and tracking.