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We are seeking a highly motivated and expert Senior Data Engineer for Framework Development who will lead the design and development of enterprise-grade testing frameworks, data quality (DQ) frameworks, and automation frameworks to strengthen the reliability and scalability of Amgen’s enterprise data ecosystem. The ideal candidate will architect reusable frameworks that enable efficient validation, continuous testing, data observability, and governance automation across data pipelines, analytics models, and platform components.
Job Responsibility:
Design, develop, and maintain testing frameworks and automation libraries for data pipelines, APIs, and ETL/ELT processes
Architect and implement Data Quality (DQ) frameworks with automated validation, anomaly detection
Develop common framework components and utilities to support cross-team use for data, integration, and analytics testing
Implement framework-driven CI/CD pipelines to automate testing, validation, and deployment of data solutions
Collaborate with data engineering teams to embed testing and quality automation within data ingestion, transformation, and consumption layers
Integrate frameworks with enterprise monitoring and observability tools to ensure proactive detection of data and system issues
Optimize framework performance, scalability, and maintainability using modular architecture and code reusability principles
Contribute to standards, guidelines, and best practices for test automation, data validation, and continuous quality engineering
Work with DevOps and platform teams to ensure seamless integration with version control, CI/CD pipelines, and infrastructure-as-code
Stay current with emerging technologies in testing, data quality engineering, and observability frameworks to continuously enhance Amgen’s engineering maturity
Requirements:
Hands-on experience in developing automation or testing frameworks using Python, PySpark, Java, or similar languages
Proficiency with testing tools and frameworks such as PyTest, unittest, Robot Framework, Great Expectations, or similar
Strong understanding of data validation, data quality, and metadata management practices
Experience in integrating frameworks into data platforms (Databricks, AWS Glue, Spark, Airflow, etc.)
Proficiency in CI/CD automation (GitHub Actions, Jenkins, or Azure DevOps)
Strong understanding of AWS services, especially data-related ones (S3, Glue, EMR, Lambda, Redshift)
Experience with test-driven development (TDD) and agile delivery practices
Excellent problem-solving, debugging, and analytical skills
Strong communication and collaboration skills to work with cross-functional engineering teams
Any degree and 8 to 13 years of experience in Computer Science, IT, or related field
Nice to have:
Experience with data observability tools (e.g., Monte Carlo, Soda, or Databand)
Knowledge of Data Fabric or Data Mesh concepts and integration with testing and DQ frameworks
Experience in API testing frameworks and contract testing (e.g., Postman, Karate, or REST Assured)
Experience with DataOps and MLOps testing practices
Familiarity with software engineering best practices, including version control, code reviews, CI/CD, and automated unit testing
Good to have domain experience in Biotech or Pharma industries
AWS Certified Developer or Data Engineer preferred