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As a Senior Data Engineer at Amgen, you will be responsible for managing and optimizing the company's data infrastructure and architecture. You will design and implement data pipelines, develop data models, perform data integration, and ensure data quality and governance. Your expertise in data engineering, big data technologies, and data manipulation will contribute to the effective storage, processing, and utilization of large-scale data sets.
Job Responsibility
Design, develop, and maintain scalable ETL/ELT pipelines to support structured, semi-structured, and unstructured data processing across the Enterprise Data Engineering for Biotech or Pharma functional knowledge of R&D
Implement real-time and batch data processing solutions, integrating data from multiple sources into a unified, governed data fabric architecture
Optimize big data processing frameworks using Apache Spark, Hadoop, or similar distributed computing technologies to ensure high availability and cost efficiency
Work with metadata management and data lineage tracking tools to enable enterprise-wide data discovery and governance
Ensure data security, compliance, and role-based access control (RBAC) across data environments
Optimize query performance, indexing strategies, partitioning, and caching for large-scale data sets
Develop CI/CD pipelines for automated data pipeline deployments, version control, and monitoring
Implement data virtualization techniques to provide seamless access to data across multiple storage systems
Collaborate with cross-functional teams, including data architects, business analysts, and DevOps teams, to align data engineering strategies with enterprise goals
Stay up to date with emerging data technologies and best practices, ensuring continuous improvement of Enterprise Data Fabric architectures
Model data for analytics and ML (star/snowflake, Data Vault, semantic layers) and implement robust ELT patterns (dbt or equivalent)
Build and maintain a lakehouse/warehouse (e.g., Delta Lake/Iceberg/Hudi
Snowflake/Redshift/BigQuery) with partitioning, clustering, and cost/perf optimization
Orchestrate workflows with Airflow/Azure Data Factory/Prefect and implement CI/CD for data (Git-based deployments, environments, automated tests)
Implement data quality and observability (Great Expectations/Deequ, expectations-as-code, lineage/metadata, SLOs and alerting with OpenTelemetry/Prometheus/Datadog)
Enforce security and governance (RBAC/ABAC, encryption, secrets, tokenization), manage PII/PHI under GDPR/CCPA and secure SDLC for data
Partner with analytics, data science, and product to define interfaces, SLAs, and contracts
publish clear docs, runbooks, and diagrams
Lead technical discovery, RFCs, and POCs
evaluate vendor tools and guide integrations
Mentor engineers
raise the bar on code quality, reviews, and engineering practices
Requirements
Bachelor’s or Master's degree with 8 - 13 years of experience in Computer Science, IT or related field
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
Strong understanding of AWS services
Experience with Data Fabric, Data Mesh, or similar enterprise-wide data architectures
Ability to quickly learn, adapt and apply new technologies
Strong problem-solving and analytical skills
Excellent communication and teamwork skills
Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices
Excellent analytical and troubleshooting skills
Strong verbal and written communication skills
Ability to work effectively with global, virtual teams
High degree of initiative and self-motivation
Ability to manage multiple priorities successfully
Team-oriented, with a focus on achieving team goals