Continental is a leading tire manufacturer and industry specialist. Founded in 1871, the company generated sales of €19.7 billion in 2025 and currently employs around 78,000 people in 54 countries and markets. ContiTech is one of the world’s leading material experts for industry applications. The Continental group sector offers its customers reliable, safe and convenient industry and service solutions using a range of materials for off-highway applications, on rails and roads, in the air, under and above the ground, in industrial environments, for the food sector and the furniture industry. With about 20,000 employees (Status as of March 2026) at more than 60 sites globally, and sales of some 6.0 billion euros (2025), the global industrial partner is active with core branches in Asia, Europe and North and South America. Key targets of the job profile * Govern the enterprise-wide standards for data & analytics modeling and performance within the Databricks Lakehouse. * Drive consistency and reuse of core data & analytics artifacts and ensure scalable integration across all business domains. * Provide expert consulting, quality assurance, and enablement for data engineering and data science teams. * Act as a design authority for data warehouse, semantic modeling, and advanced analytics integration. Dimensions/ Organization/Magnitude * Acts as the senior engineering point of contact for the lakehouse layer across global teams. * Coordinates with 25+ data engineering and data science professionals across domains and geographies. * Collaborates closely with platform architects, data scientists, governance teams, and functional IT globally. Main Tasks: Data & Analytics Model Governance • Define enterprise 3NF and warehouse modeling standards. • Maintain and review enterprise-wide data & analytics models and shared artifacts. • Align naming conventions and metadata handling with governance standards. Lakehouse Performance & Semantics • Guide partitioning, indexing, and performance tuning. • Enable, steer and optimize semantic integration with Power BI, live tabular exploration and other tools. • Own common functions, e.g. FX conversion, BOM logic, time-slicing. Quality Gate & Design Authority • Review and approve core components for quality and reusability. • Provide support on high-performance or high-complexity challenges. • Align lakehouse implementation with architectural decisions. Advanced Analytics Integration • Collaborate with data science and AI teams on model deployment. • Ensure seamless integration of ML/AI pipelines into the lakehouse. • Support LLM and external API integration patterns. Developer Enablement & Reusability • Build and maintain shared libraries and data engineering templates.  • Coach junior engineers and define TDD and "as-code" standards. • Drive engineering excellence across the community of practice. Documentation & Standards • Maintain architectural blueprints, templates, and best practices. • Publish design guidelines and coding standards. • Create re-usable architecture patterns for lakehouse environments. Performance & Cost Optimization • Monitor usage and implement auto-scaling policies. • Analyze and optimize cluster configurations for cost-efficiency. • Provide cost transparency and usage reporting to stakeholders. Summary: This role is pivotal in shaping the semantic and performance foundation of the enterprise-wide data & analytics platform. By driving standardization and governance in core modeling practices, this position ensures that data engineering and data science teams can build scalable, high-performing, and reusable solutions aligned with business and analytics goals. Education / Certification: Degree in Computer Science or related field; certifications in Databricks or Microsoft preferred. Professional Experience: 6–10 years in data engineering with focus on enterprise data & analytics warehouse, lakehouse modeling, and ML integration. Project and/or Process Experience: Hands-on experience designing large-scale semantic, warehouse, and advanced analytics layers. Leadership Experience: Track record of architectural ownership and peer enablement with diverse teams. Intercultural / International Experience: Experience working in international teams across multiple time zones and cultures, preferably with teams in India, Germany, and the Philippines. Required skills:  • PySpark, Scala, Delta Lake, Unity Catalog, Power BI integration                • Dimensional and 3NF modeling, semantic layer design                      • ML model lifecycle integration, MLOps patterns, LLM APIs                         • Test-driven development, performance profiling                   • Metadata strategy, lineage tracking, naming standards        • Cross-domain business logic patterns (e.g., KPIs, hierarchies)            • Reporting & BI tools (e.g. tabular analysis tools) and dashboarding tools (preferably Power BI)  • Advanced analytics (Data Mining & other KDD) and machine learning concepts and methodologies    • AI service (e.g. LLM) & API integration concepts What we offer: The 13-th salary – Paid once a year, in December; Meal tickets - With a value of 40 Ron; Private Health Insurance – Health is the most important, so we offer you a medical subscription through Signal Iduna; Referral bonuses - We encourage colleagues to refer new candidates to us and, at the same time, to get the chance to receive a bonus; Bookster – Feed your body and your mind. You can borrow books and you’ll receive them at the office; Sports benefits- It’s important to stay active, so we offer you the 7Card; Discounts at our partners – We collaborate with different vendors, and