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:

Amgen is seeking a Data Engineer to design, develop, and support scalable enterprise data and integration solutions that enable analytics, reporting, and business decision-making. This role focuses on building and maintaining cloud-native data pipelines, APIs, and integration platforms using technologies such as Databricks, Apache Spark, MuleSoft, Airflow, and AWS.

The ideal candidate will have strong experience in big data processing, ETL/ELT development, API integration, workflow orchestration, and cloud data engineering practices. The successful candidate should possess strong problem-solving skills, a passion for data engineering, and the ability to work effectively in agile product delivery environments.

Roles & Responsibilities:

Design, develop, and maintain scalable data pipelines and cloud-native data solutions for data ingestion, transformation, processing, and distribution using Databricks, Apache Spark, Airflow, and AWS technologies.
Design, build, and maintain MuleSoft APIs and integration solutions leveraging API-led connectivity and enterprise integration best practices to enable robust integrations between CRM and cross-functional enterprise systems.
Develop and optimize ETL/ELT processes to support large-scale structured and unstructured data processing across multiple enterprise platforms.
Participate in the end-to-end delivery of data engineering solutions including design, development, testing, deployment, monitoring, and operational support.

Collaborate with cross-functional teams to understand data requirements and design solutions that meet business needs

Build scalable and reusable data processing frameworks, components, and integration services following enterprise engineering standards and best practices.
Develop and maintain data models, metadata definitions, data dictionaries, and technical documentation to ensure data consistency, accuracy, and governance compliance.
Implement data validation, reconciliation, and quality control processes to ensure reliability and integrity of enterprise data assets.

Optimize Spark jobs, SQL queries, APIs, and ETL pipelines for scalability, reliability, and performance.
Leverage AWS cloud services to develop scalable, resilient, and secure data solutions and integration services.
Implement monitoring, logging, alerting, and operational support processes for data pipelines, APIs, and cloud-based integrations.
Support CI/CD automation and DevOps best practices for data engineering and integration deployments.
Identify, troubleshoot, and resolve complex data processing, integration, and performance-related issues in a timely manner.
Research and evaluate emerging technologies, tools, and AI-assisted engineering capabilities that improve platform performance, developer productivity, and operational excellence.
Adhere to software engineering best practices including coding standards, unit testing, reusable component design, peer reviews, and documentation standards.
Participate in sprint planning, backlog refinement, estimation, and other agile software delivery activities.

Basic Qualifications and Experience:

Master’s / Bachelor’s degree 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, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing

Proficiency in data analysis tools (eg. SQL) and experience with data visualization tools

Skilled in MuleSoft API and Mulesoft integration job design and development

Excellent problem-solving skills and the ability to work with large, complex datasets

Strong understanding of data governance frameworks, tools, and best practices.

Knowledge of data protection regulations and compliance requirements (e.g., GDPR, CCPA)

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

Knowledge of AnyPoint Platform, Python/R, Databricks, SageMaker, Airflow, AWS cloud data platforms

Proficiency in using Databricks Assistant, and other AI tools

Professional Certifications

Certified in MuleSoft Certified Developer 1 or higher (preferred)

Certified Associate or Professional Data Engineer / Data Analyst (preferred on Databrick)

Certified AWS Professional or Associate certification (preferred)

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

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.

We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Summary

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See also