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Merck KGaA
RoleThe ability to easily find, access, and analyze data across an organization is key for every modern business to be able to efficiently make decisions, optimize processes, and to create new business models.As a Sr Data Engineer in the Digital & Data team you will work hands-on to deliver and maintain the pipelines required by the business functions to derive value from their data. For this, you will bring data from a varied landscape of source systems into our cloud-based analytics stack and implement necessary cleaning and pre-processing steps in close collaboration with our business customers. Furthermore, you will work closely together with our teams to ensure that all data assets are governed according to the FAIR principles. To keep the engineering team scalable, you and your peers will create reusable components, libraries, and infrastructure that will be used to accelerate the pace with which future use-cases can be delivered.What we offerYou will be part of a team dedicated to delivering state-of-the-art solutions for enabling data analytics use cases across the Healthcare sector of a leading, global Science & Technology company. As such, you will have the unique opportunity to gain insight into our diverse business functions allowing you to expand your skills in various technical, scientific, and business domains. Working in a project-based way covering a multitude of data domains and technological stacks, you will be able to significantly develop your skills and experience as a Data Engineer. Who you areBE/M.Sc./PhD in Computer Science or related field and 8+ years of work experience in a relevant capacityExperience in working with cloud environments such as, Hadoop, AWS, GCP, and Azure.Experience with enforcing security controls and best practices to protect sensitive data within AWS data pipelines, including encryption, access controls, and auditing mechanisms.Agile mindset, a spirit of initiative, and desire to work hands-on together with your teamInterest in solving challenging technical problems and developing the future data architecture that will enable the implementation of innovative data analytics use-casesExperience in leading small to medium-sized teamsExperience in creating architectures for ETL processes for batch as well as streaming ingestionKnowledge of designing and validating software stacks for GxP relevant contexts as well as working with PII dataFamiliarity with the data domains covering the Pharma value-chain (e.g. research, clinical, regulatory, manufacturing, supply chain, and commercial)Strong, hands-on experience in working with Python, Pyspark & R codebases, proficiency in additional programming languages (e.g. C/C++, Rust, Typescript, Java, …) is expectedKnowledge of database technologies for OLTP and OLAP workloads and a firm grasp of SQLExperience working with Apache Spark and the Hadoop ecosystemWorking with heterogenous compute environments and multi-platform setupsExperience in working with cloud environments such as, Hadoop, AWS, GCP, and AzureBasic knowledge of Statistics and Machine Learning algorithms is favorableThis is the respective role description:The ability to easily find, access, and analyze data across an organization is key for every modern business to be able to efficiently make decisions, optimize processes, and to create new business models. The Data Architect plays a key role in unlocking this potential by defining and implementing a harmonized data architecture for Healthcare. Together with a team of Data Engineers, the Data Architect is responsible for the delivery and maintenance of reliable data pipelines for the ingestion of data from a heterogenous ecosystem as well as the creation of specialized, cloud-based data processing and analytics stacks. He/she will work hand in hand with our Data Governance team to ensure that all data assets are covered by our governance process. During the process the Data Architect will collaborate closely with our business, corporate functions, as well as out IT organization, in order to gather requirements and shape a global framework of engineering best practices.
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