Data Engineer - R&D Multi-Omics (Open)

Role Overview:

We are seeking a Data Engineer to support software development and data engineering efforts for target and biomarker discovery. This role plays a crucial part in building and maintaining robust data pipelines, enabling scientific research and data analytics for Amgen's R&D teams. The engineer will work closely with global cross-functional teams, including research scientists, to develop data solutions using modern technologies and cloud platforms.

Roles & Responsibilities:

• Design, develop, and implement ETL/ELT processes and data integration solutions for large-scale scientific data.
• Lead data pipeline projects from inception to deployment, managing timelines, risks, and deliverables.
• Collaborate with scientists and global teams to understand data requirements and develop tailored solutions.
• Develop and maintain data models, data dictionaries, and technical documentation.
• Optimize large datasets for performance and enable efficient querying.
• Build scalable, secure, and reusable data pipelines using cloud-native services (AWS preferred).
• Apply best practices for coding, testing, and software component reuse.
• Implement data privacy and security controls for sensitive R&D datasets.
• Participate in sprint planning and provide technical estimations for implementation tasks.
• Stay current with new technologies to enhance ETL platform capabilities.
• Maintain technical documentation and diagrams using Confluence and tools like Lucidchart or Miro.

Basic Qualifications:

• Bachelor's or Master's degree in Computer Science, IT, Computational Chemistry, Computational Biology, Bioinformatics, or related field.
• 5 to 9 years of relevant experience in scientific data engineering or software development.

Preferred Qualifications:

• 5+ years of experience in designing and supporting scientific data platforms in biopharma or biotech research environments.

Must-Have Skills:

• Proficiency in SQL and Python for data engineering and test automation (e.g., pytest).
• Experience with big data platforms and tools like Databricks, Apache Spark (PySpark, SparkSQL), and workflow orchestration tools.
• Ability to work with large, complex datasets and troubleshoot performance issues.

Good-to-Have Skills:

• Experience with Git, CI/CD pipelines, and the software development lifecycle.
• Familiarity with relational databases (PostgreSQL, MySQL, Oracle).
• Experience working with AWS or similar cloud platforms.
• Working knowledge of Agile methodologies.
• Background in data modeling, warehousing, and data integration concepts.
• Hands-on experience with data visualization tools like Dash, Plotly, or Spotfire.
• Experience with collaboration and diagramming tools like Miro or Lucidchart.
• Experience with writing and maintaining technical documentation in Confluence.

Professional Certifications:

• Databricks Certified Data Engineer Professional (preferred)

Soft Skills:

• Excellent analytical and critical-thinking skills.
• Strong collaboration and communication skills.
• Self-motivated and proactive in problem solving.
• Ability to manage multiple tasks and priorities effectively.
• Team-oriented and focused on shared success.
• Strong presentation and documentation abilities.