.The Straumann Group (SIX: STMN) is a global leader in tooth replacement and orthodontic solutions that restore smiles and confidence. It unites global and international brands that stand for excellence, innovation, and quality in replacement, corrective, and digital dentistry, including Anthogyr, ClearCorrect, Medentika, Neodent, NUVO, Straumann, and other fully/partly owned companies and partners. In collaboration with leading clinics, institutes, and universities, the Group researches, develops, manufactures, and supplies dental implants, instruments, CADCAM prosthetics, orthodontic aligners, biomaterials, and digital solutions for use in tooth correction, replacement, and restoration or to prevent tooth loss.Headquartered in Basel, Switzerland, the Group currently employs more than 11,000 people worldwide. Its products, solutions, and services are available in more than 100 countries.The Straumann Group unites global reach, experience, and innovation with passion and a commitment for uncompromising quality, making enhanced dental healthcare available and accessible to customers and patients around the globe. People and culture are the Straumann Group's greatest assets; they are the keys to high performance and sustainable success. We strive for a culture that builds trust and collaboration, fosters diversity, embraces change, promotes agility, learning, and innovation, creates opportunities, allows people to make mistakes, and encourages them to take both responsibility and ownership.The E2E Supply Chain – Data Engineer will prepare data for use by analysts, data scientists, and self-service analytics internal consumers via analytics tools by filtering, tagging, joining, parsing, and normalizing and building out E2E supply chain data lake. You will also maintain processes, standards, and policies needed to ensure high-quality master and transactional data, consistent with Straumann Group's Digital practices and E2E supply chain strategy.Main Tasks and Responsibilities Supply Chain Data Optimization: Utilize data preparation and modeling techniques to optimize supply chain data for self-service analytics, product development, modeling, and data discovery. This includes filtering, tagging, joining, parsing, and normalizing supply chain data.Standardization Across the Supply Chain: Harmonize and standardize supply chain data from various sources, including structured, unstructured, and waveform data.Supply Chain Data Enrichment: Identify new variables and insights that can enhance curated supply chain data sets and models.Data Management: Proficiently organize and enhance data to facilitate analytics, modeling, and product development. This involves establishing essential data pipelines, standardizing data from diverse sources, and generating meaningful insights.Data Warehousing: Design and manage data warehouses to ensure scalable, reliable, and performant data storage solutions