Data Engineer

Data Engineer

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.

Role Description

Let’s do this. Let’s change the world.

We are seeking an experienced Data Engineer to design, develop, and support scalable data pipelines and integration solutions. This role will work with large and complex datasets to ensure data is reliable, accessible, secure, and available for analytics, reporting, and AI use cases.

The ideal candidate has strong hands-on experience with Databricks, Apache Spark, Python, SQL, cloud technologies, data modeling, and ETL/ELT processes. The candidate will collaborate with data architects, business teams, data scientists, product teams, and DevOps teams to deliver high-quality data solutions.

Experience in biotechnology, pharmaceutical, manufacturing, life sciences, or another regulated industry is preferred.

Roles and Responsibilities

• Design, develop, test, and maintain scalable data pipelines and data-integration solutions.

• Build ETL/ELT processes for structured, semi-structured, and unstructured data.

• Integrate data from enterprise applications, databases, APIs, cloud platforms, and third-party systems.

• Contribute to the technical design and implementation of end-to-end data solutions.

• Develop reusable and maintainable Python, PySpark, Spark SQL, and SQL components.

• Implement data-quality checks, validation rules, reconciliation processes, logging, and exception handling.

• Optimize Spark workloads, SQL queries, partitioning, and data-processing performance.

• Develop and maintain data models, data dictionaries, mappings, and technical documentation.

• Implement data-security, privacy, governance, and role-based access requirements.

• Support workflow orchestration, scheduling, monitoring, alerting, and recovery processes.

• Contribute to CI/CD pipelines, automated testing, version control, and deployment processes.

• Troubleshoot data-pipeline failures, performance issues, and data-quality problems.

• Collaborate with data architects, business SMEs, analysts, data scientists, product teams, and DevOps teams.

• Participate in sprint planning, backlog refinement, technical estimation, and delivery activities.

• Take ownership of assigned data-engineering work from development through deployment and production support.

• Evaluate new technologies and recommend improvements to da