Job
Description
ABOUT THE ROLE You will play a key role in a regulatory submission content automation initiative which will modernize and digitize the regulatory submission process, positioning Amgen as a leader in regulatory innovation. The initiative leverages state-of-the-art technologies, including Generative AI, Structured Content Management, and integrated data to automate the creation, review, and approval of regulatory content. ? The role is responsible for sourcing and analyzing data for this initiative and support designing, building, and maintaining the data pipelines to drive business actions and automation . This role involves working with Operations source systems, find the right data sources, standardize data sets, supporting data governance to ensure data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture and ETL processes Roles & Responsibilities Ensure reliable , secure and compliant operating environment. Identify , extract, and integrate required business data from Operations systems residing in modern cloud-based architectures. Design, develop, test and maintain scalable data pipelines, ensuring data quality via ETL/ELT processes. Schedul e and manag e workflows the ensure pipeline s run on schedule and are monitored for failures. Implement data integration solutions and manage end-to-end pipeline projects, including scope, timelines, and risk. Reverse-engineer schemas and explore source system tables to map local representations of target business concepts. Navigate application UIs and backends to gain business domain knowledge and detect data inconsistencies. Break down information models into fine-grained, business-contextualized data components. Work closely with cross-functional teams, including product teams, data architects, and business SMEs, to understand requirements and design solutions. Collaborate with data scientists to develop pipelines that meet dynamic business needs across regions. Create and maintain data models, dictionaries, and documentation to ensure accuracy and consistency. Adhere to SOPs, GDEs , and best practices for coding, testing, and reusable component design. Basic Qualifications and Experience Master’s degree and 1 to 3 years of Computer Science, IT or related field experience OR Bachelor’s degree and 3 to 5 years of Computer Science, IT or related field experience OR Diploma and 7 to 9 years of Computer Science, IT or related field experience Functional Skills: Must-Have Skills: Hands on experience with data practices, technologies , and platforms , such as Databricks, Python, Prophecy, Gitlab, LucidChart etc Proficiency in data analysis tools ( eg. SQL) and experience with data sourcing tools Excellent problem-solving skills and the ability to work with large, complex datasets U nderstanding of data governance frameworks, tools, and best practices. Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA) Good-to-Have Skills: Experience with ETL tools and various Python packages related to data processing, machine learning model development Strong understanding of data modeling, data warehousing, and data integration concepts Knowledge of Python /R , Databricks, cloud data platforms Professional Certifications Certified Data Engineer / Data Analyst (preferred on Databricks ) Soft Skills: Excellent critical-thinking and problem-solving skills Strong communication and collaboration skills Demonstrated awareness of how to function in a team setting Demonstrated presentation skills
