Responsibilities 

• Technical Leadership: Provide strategic and technical guidance to the data engineering team. Lead initiatives to improve data quality, reliability, and infrastructure efficiency. 

• Develop & Maintain Advanced Data Systems: Architect and maintain advanced, scalable ETL processing pipelines for batch and near-real-time data, including analytical and machine learning workflows using a modern technology stack (Airflow, Kubernetes, Starburst, dbt, Kafka). 

• Enterprise Data Architecture: Design and continuously extend our enterprise data warehouse to align with business goals, supporting the company's analytics-driven business philosophy. 

• Optimization and Enhancement: Focus on optimizing and enhancing our data infrastructure using Starburst, Iceberg, Microsoft Fabric, and Looker. Ensure these tools work seamlessly together to deliver high-performance data processing and analytics capabilities. 

• Integration and Collaboration: Collaborate closely with different teams to ensure the integration of these tools aligns with business goals and operational objectives. 

• Performance Improvement: Lead efforts to improve the performance of existing systems, ensuring robust, efficient, and scalable data solutions that meet the needs of diverse stakeholders. 

• Result-Driven Initiatives: Drive initiatives that leverage the full potential of our data stack to deliver actionable insights and support data-driven decision-making processes across the business. 

• Automation & CI/CD Pipeline Management: Lead automation initiatives that allow code generation for pipelines and expand our CI/CD capabilities using GitLab pipelines to enhance testing and deployment automation. 

• Cross-Team Collaboration: Work closely with cross-functional teams to understand business requirements and produce data solutions that aid in impactful decision making. 

• Engagement and Consultation: Engage with business units to ensure technical solutions support business objectives and improve decision-making processes. 

Qualifications 

• Advanced Experience in Data Engineering: Proven expertise in designing and implementing data solutions, with a minimum Bachelor's degree in a quantitative subject (computer science, physics, mathematics, engineering, etc.). 

• Technical Proficiency: Extensive knowledge of SQL, particularly with dbt, along with an understanding of RDBMS and Columnar Database concepts. Proficiency in Python and experience with Linux platforms are essential. 

• Cloud Architecture Experience: Experience with cloud data architectures (AWS, Azure) preferred. 

• Adaptability and Innovation: Continuous learner with a strong inclination to stay updated with emerging technologies. Capable of pioneering innovative data solutions to maintain competitive advantage. 

• Communication and Leadership: Excellent communication skills in English (German is a plus), with the ability to work independently and collaboratively, leading by example and enhancing team synergy and performance. 

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