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Merck KGaA
You will be joining the Life Science Analytics Center of Excellence (ACE), a strategic partner of the Life Science business sector, providing tailored data & AI solutions and empowering data-driven decisions. By leveraging advanced analytics, AI, and machine learning , ACE supports the Life Science community in optimizing processes, driving data governance, and fostering a culture of continuous improvement. We are seeking a highly skilled and experienced Solution Architect to craft data models that power our analytics products and to help us achieve our vision of a broadly reusable data layer. Translating business requirements/semantics into data models to enable Data Products in Palantir Foundry. Develop documentation of data models on different levels (conceptual, logical, physical)Identify and enable data from multiple ERP systems needed for respective data models. Introducing data models to data engineers to enable. Determine data flow of to-be implemented data models (definition of data transformations at what points of the physical data model)Oversee the development efforts and validate that they lead to an accurate outcome (according to data model). Overseeing the data backend of our existing and future analytics applications in the Supply Chain domain to ensure a maximum of reusability of data assetsEnsuring compliance with existing data governance and security policies in Foundry. Maintain conversations with technical SMEs (e.g. architects on the SAP side). Provide technical guidance and mentorship to team members, especially non-technical staff (business SMEs, PMOs) Who you are:Bachelor’s or master’s degree in a quantitative or technical field (math, statistics, computer science, data & analytics5+ years of experience designing, developing, operationalizing, and maintaining data and analytics applications at enterprise scale. Experience working with ERP data tables (SAP or Oracle)Good experience with tools for data modeling, warehousing, business intelligence, and databases (e.g. Tableau , Hadoop, SAS, Business Objects, ORACLE, and Hive. Experience in working with unstructured, semi-structured, and structured data
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