Career Category
Engineering
Job Description
ABOUT AMGEN
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE
Role Description:
The role is responsible for developing and maintaining the data architecture of the Enterprise Data Fabric. Data Architecture includes the activities required for data flow design, data modeling, physical data design, query performance optimization.
The Data Modeler position is responsible for developing business information models by studying the business, our data, and the industry. This role involves creating data models to realize a connected data ecosystem that empowers consumers. The Data Modeler drives cross-functional data interoperability,



enables effective decision-making, and supports AI usage of Foundational Data.
Roles & Responsibilities:
- Develop and maintain conceptual logical, and physical data models and to support business needs
- Contribute to and Enforce data standards, governance policies, and best practices
- Design and manage metadata structures to enhance information retrieval and usability
- Maintain comprehensive documentation of the architecture, including principles, standards, and models
- Evaluate and recommend technologies and tools that best fit the solution requirements
- Drive continuous improvement in the architecture by identifying opportunities for innovation and efficiency

Basic Qualifications and Experience:
- Doctorate / Master’s / Bachelor’s degree with 8-12 years of experience in Computer Science, IT or related field

Functional Skills:
Must-Have Skills
- Data Modeling: Proficiency in creating conceptual, log

📌 Data Modeler (Hyderabad)
🏢 AMGEN
📍 Hyderabad