This role is a key part of Amgen’s Global Supply Chain Analytics team, developing data and AI-driven solutions for customer service. Responsibilities include designing, building, and managing data, analyzing it for actionable insights, creating reports, supporting data governance, and visualizing information to ensure reliable and efficient data management. The ideal candidate has strong technical skills, experience with big data, and a solid understanding of data architecture and ETL processes.
Roles & Responsibilities:
Design, develop, and maintain data solutions for data generation, collection, and processing
Be a key team member that assists in design and development of the data pipeline
Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems
Contribute to the creation of metric dashboards, self-serve analytics, and business insights using Tableau or Power BI
Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency
Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions
Adhere to best practices for coding, testing, and designing reusable code/component
Exploratory data analysis, understand business knowledge, do feature engineering
Support generation of annual product review documents, regulatory inspections, and process documentation
Work with data engineers on data quality assessment, data cleansing and data analytics
Basic Qualifications and Experience:
Master’s degree and 5 to 8 years of Computer Science, IT or related field experience OR
Bachelor’s degree and 5 to 8 years of Computer Science, IT or related field experience OR
Diploma and 5 to 8 years of Computer Science, IT or related field experience
Functional Skills:
Must-Have Skills
Hands on experience with big data technologies and platforms, such as Databricks (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing
Hands on experience with various Python/R packages for EDA, feature engineering and machine learning model training
Proficiency in data analysis tools (eg. SQL) and experience with data visualization tools
Proficiency with data visualization platforms like Tableau, Power BI, or matplotlib/seaborn for Python
Excellent problem-solving skills and the ability to work with large, complex datasets
Good-to-Have Skills:
Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development
Strong understanding of data modeling, data warehousing, and data integration concepts
Interest in GenAI/LLM technology and building data products that support natural language interfaces
Professional Certifications:
Certified Data & Analytics professional
Soft Skills:
Excellent critical-thinking and problem-solving skills
Strong communication and collaboration skills
Demonstrated awareness of how to function in a team setting
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