In this vital role you will be responsible for designing, building, maintaining, analyzing, and interpreting data to provide actionable insights that drive business decisions. This role involves working with large datasets, developing reports, supporting and executing data initiatives and, visualizing data 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
- Design, build, and support data ingestion, transformation, and delivery pipelines across structured and unstructured sources within the enterprise data engineering.
- Manage and monitor day-to-day operations of the data engineering environment, ensuring high availability, performance, and data integrity.
- Collaborate with data architects, data governance, platform engineering, and business teams to support data integration use cases across R&D, Clinical, Regulatory, and Commercial functions.
- Integrate data from laboratory systems, clinical platforms, regulatory systems, and third-party data sources into enterprise data repositories.
- Implement and maintain metadata capture, data lineage, and data quality checks across pipelines to meet governance and compliance requirements.
- Support real-time and batch data flows using technologies such as Databricks, Kafka, Delta Lake, or similar.
- Work within GxP-aligned environments, ensuring compliance with data privacy, audit, and quality control standards.
- Partner with data stewards and business analysts to support self-service data access, reporting, and analytics enablement.
- Maintain operational documentation, runbooks, and process automation scripts for continuous improvement of data fabric operations.
- Participate in incident resolution and root cause analysis, ensuring timely and effective remediation of data pipeline issues.
- Create documentation, playbooks, and best practices for metadata ingestion, data lineage, and catalog usage.
- Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
- Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories.
- Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
- Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
Must have Skills: - Build and maintain data pipelines to ingest and update metadata into enterprise data catalog platforms in biotech or life sciences or pharma.
- Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
- Proficiency in workflow orchestration, performance tuning on big data processing.
- experience in data engineering, data operations, or related roles, with at least 2+ years in life sciences, biotech, or pharmaceutical environments.
- Experience with cloud platforms (e.g., AWS, Azure, or GCP) for data pipeline and storage solutions.
- Understanding of data governance frameworks, metadata management, and data lineage tracking.
- Strong problem-solving skills, attention to detail, and ability to manage multiple priorities in a dynamic environment.
- Effective communication and collaboration skills to work across technical and business stakeholders.
- Strong problem-solving and analytical skills
- Excellent communication and teamwork skills
- Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.
Preferred Qualifications: Data Engineering experience in Biotechnology or pharma industry Experience in writing APIs to make the data available to the consumers Experienced with SQL/NOSQL database, vector database for large language models Experienced with data modeling and performance tuning for both OLAP and OLTP databases Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and Dev Ops
Basic Qualifications: Master s degree and 3 to 4 + years of Computer Science, IT or related field experience Bachelor s degree and 5 to 8 + years of Computer Science, IT or related field experience Diploma and 7 to 9 years of Computer Science, IT or related field experience
Professional Certifications: - AWS Certified Data Engineer preferred
- Databricks Certificate preferred
- Scaled Agile SAFe certification preferred
Soft Skills :
- Excellent verbal and written communication skills.
- High degree of professionalism and interpersonal 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
What you can expect of us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we ll support your journey every step of the way.
In addition to the base salary, Amgen offers competitive and comprehensive Total Rewards Plans that are aligned with local industry standards.
