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Join Amgen’s Mission of Serving Patients. At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.
Job Responsibility
Responsible for managing and optimizing the company's data infrastructure and architecture for Clinical Data Hub, including cloud-based distributed compute and storage
Design and implement data pipelines, develop data models, perform data integration, and ensure data quality and governance
Contributes to the effective storage, processing, and utilization of large-scale data sets
Work with Product Owner, Product Management, Agile Team, and Data Architects to plan and deliver prioritized Clinical Data Hub backlog items and optimize the technical environment
Ensure data requirements for applications, data scientists, and cross-functional use cases are met, and ensure high performance and reliability of the system
Provide insights and actionable solutions for business clients.
Requirements
Master’s degree (or foreign equivalent) in Computer Science, Computer Engineering, Clinical Informatics or related field and 2 years of experience in the job offered or in a Data Engineer – related occupation. The position requires 2 years of experience in the following: Manage and tune Hadoop, MapReduce, or Spark SQL
Build ETL pipelines to transfer data
Optimize database for usage
Manage data stewardship and governance
Configure cloud services to orchestrate, monitor, and run end-to-end data warehousing solutions
and Model data for translation, linking, and consumption from source to data warehouse.