Software Development Engineer III

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 45 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE

Role Description:

We are seeking a highly experienced and hands-on Test Automation Engineering Manager with strong leadership skills and deep expertise in Data Integration, Data Quality, and automated data validation across real-time and batch pipelines. In this strategic role, you will lead the design, development, and implementation of scalable test automation frameworks that validate data ingestion, transformation, and delivery across diverse sources into AWS-based analytics platforms, leveraging technologies like Databricks, PySpark, and cloud-native services.

As a lead, you will drive the overall testing strategy, lead a team of test engineers, and collaborate cross-functionally with data engineering, platform, and product teams. Your focus will be on delivering high-confidence, production-grade data pipelines with built-in validation layers that support enterprise analytics, ML models, and reporting platforms.

The role is highly technical and hands-on, with a strong focus on automation, metadata validation, and ensuring data governance practices are seamlessly integrated into development pipelines.

Roles & Responsibilities: 

• Define and drive the test automation strategy for data pipelines, ensuring alignment with enterprise data platform goals.

• Lead and mentor a team of data QA/test engineers, providing technical direction, career development, and performance feedback.

• Own delivery of automated data validation frameworks across real-time and batch data pipelines using Databricks and AWS services.

• Collaborate with data engineering, platform, and product teams to embed data quality checks and testability into pipeline design.

• Design and implement scalable validation frameworks for data ingestion, transformation, and consumption layers.

• Automate validations for multiple data formats including JSON, CSV, Parquet, and other structured/semi-structured file types during ingestion and transformation.

• Automate data testing workflows for pipelines built on Databricks/Spark, integrated with AWS services like S3, Glue, Athena, and Redshift.

• Establish reusable test components for schema validation, null checks, deduplication, threshold rules, and transformation logic.

• Integrate validation processes with CI/CD pipelines, enabling automated and event-driven testing across the development lifecycle.

• Drive the selection and adoption of tools/frameworks that improve automation, scalability, and test efficiency.

• Oversee testing of data visualizations in Tableau, Power BI, or custom dashboards, ensuring backend accuracy via UI and data-layer validations.

• Ensure accuracy of API-driven data services, managing functional and regression testing via Postman, Python, or other automation tools.

• Track test coverage, quality metrics, and defect trends, providing regular reporting to leadership and ensuring continuous improvement.

• establishing alerting and reporting mechanisms for test failures, data anomalies, and governance violations.

• Contribute to system architecture and design discussions, bringing a strong quality and testability lens early into the development lifecycle.

• Lead test automation initiatives by implementing best practices and scalable frameworks, embedding test suites into CI/CD pipelines to enable automated, continuous validation of data workflows, catalog changes, and visualization updates

• Mentor and guide QA engineers, fostering a collaborative, growth-oriented culture focused on continuous learning and technical excellence.

• Collaborate cross-functionally with product managers, developers, and DevOps to align quality efforts with business goals and release timelines.

• Conduct code reviews, test plan reviews, and pair-testing sessions to ensure team-level consistency and high-quality standards.

Must-Have Skills:

• Hands-on experience with Databricks and Apache Spark for building and validating scalable data pipelines

• Strong expertise in AWS services including S3, Glue, Athena, Redshift, and Lake Formation

• Proficient in Python, PySpark, and SQL for developing test automation and validation logic

• Experience validating data from various file formats such as JSON, CSV, Parquet, and Avro

• In-depth understanding of data integration workflows including batch and real-time (streaming) pipelines

• Strong ability to define and automate data quality checks: schema validation, null checks, duplicates, thresholds, and transformation validation

• Experience designing modular, reusable automation frameworks for large-scale data validation

• Skilled in integrating tests with CI/CD tools like GitHub Actions, Jenkins, or Azure DevOps

• Familiarity with orchestration tools such as Apache Airflow, Databricks Jobs, or AWS Step Functions

• Hands-on experience with API testing using Postman, pytest, or custom automation scripts

• Proven track record of leading and mentoring QA/test engineering teams

• Ability to define and own test automation strategy and roadmap for data platforms

• Strong collaboration skills to work with engineering, product, and data teams

• Excellent communication skills for presenting test results, quality metrics, and project health to leadership

• Contributions to internal quality dashboards or data observability systems

•  Awareness of metadata-driven testing approaches and lineage-based validations

• Experience working with agile Testing methodologies such as Scaled Agile.

• Familiarity with automated testing frameworks like Selenium, JUnit, TestNG, or PyTest.

Good-to-Have Skills:

• Experience with data governance tools such as Apache Atlas, Collibra, or Alation

• Understanding of DataOps methodologies and practices

• Familiarity with monitoring/observability tools such as Datadog, Prometheus, or  CloudWatch

•  Experience building or maintainingtest data generators

Education and Professional Certifications

• Bachelor’s/Masters degree in computer science and engineering preferred

Soft Skills:

• 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

• Strong presentation and public speaking skills.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.

We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.