Software Development Test Engineer
Amgen
Software Development Test Engineer
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain automated test scripts for data pipelines, ETL jobs, and data integrations.
- Validate data accuracy, completeness, transformations, and integrity across multiple systems.
- Collaborate with data engineers to define test cases and establish data quality metrics.
- Develop reusable test automation frameworks and CI/CD integrations (e.g., Selenium, Jenkins, GitHub Actions).
- Perform performance and load testing for data systems.
- Maintain test data management and data mocking strategies.
- Identify and track data quality issues, ensuring timely resolution.
- Perform root cause analysis and drive corrective actions.
- Contribute to QA ceremonies (standups, planning, retrospectives) and drive continuous improvement in QA processes and culture.
- Build and automate end-to-end data pipeline validations across ingestion, transformation, and consumption layers using Databricks, Apache Spark, and AWS services such as S3, Glue, Athena, and Lake Formation.
What we expect of you
- Master’s degree with 4 - 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree with 6 - 8 years of experience in Computer Science, IT or related field
- Certifications in Databricks, AWS, Azure, or data QA (e.g., ISTQB).
- AWS Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments)
Must-Have Skills
- Experience in QA roles, with strong exposure to data pipeline validation and ETL Testing.
- Hands-on expertise in front-end Node.js testing and performance testing of infrastructure components.
- Validate data accuracy, transformations, schema compliance, and completeness across systems using PySpark and SQL.
- Strong hands-on experience with Python, and optionally PySpark, for developing automated data validation scripts.
- Proven experience in validating ETL workflows, with a solid understanding of data transformation logic, schema comparison, and source-to-target mapping.
- Experience working with data integration and processing platforms like Databricks/Snowflake, AWS EMR, Redshift etc…
- Experience in manual and automated testing of data pipelines executions for both batch and real-time data pipelines.
- Perform performance testing of large-scale complex data engineering pipelines.
- Ability to troubleshoot data issues independently and collaborate with engineering teams for root cause analysis
- Strong understanding of QA methodologies, test planning, test case design, and defect lifecycle management.
- Hands-on experience with API testing using Postman, pytest, or custom automation scripts
- Experience integrating automated tests into CI/CD pipelines using tools like Selenium, JUnit, Jenkins, GitHub Actions, or similar.
- Knowledge of cloud platforms such as AWS, Azure, GCP.
- Understanding of data privacy, compliance, and governance frameworks.
- Knowledge of UI automated testing frameworks like Selenium, JUnit, TestNG
- Familiarity with monitoring/observability tools such as Datadog, Prometheus, or Cloud Watch
- Frameworks like Mocha/Jest and performance testing tools like JMeter or k6
- 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
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
20,000 - 40,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Experience in QA roles, with strong exposure to data pipeline validation and ETL Testing.intermediate
- Hands-on expertise in front-end Node.js testing and performance testing of infrastructure components.intermediate
- Validate data accuracy, transformations, schema compliance, and completeness across systems using PySpark and SQL.intermediate
- Strong hands-on experience with Python, and optionally PySpark, for developing automated data validation scripts.intermediate
- Proven experience in validating ETL workflows, with a solid understanding of data transformation logic, schema comparison, and source-to-target mapping.intermediate
- Experience working with data integration and processing platforms like Databricks/Snowflake, AWS EMR, Redshift etc…intermediate
- Experience in manual and automated testing of data pipelines executions for both batch and real-time data pipelines.intermediate
- Perform performance testing of large-scale complex data engineering pipelines.intermediate
- Ability to troubleshoot data issues independently and collaborate with engineering teams for root cause analysisintermediate
- Strong understanding of QA methodologies, test planning, test case design, and defect lifecycle management.intermediate
- Hands-on experience with API testing using Postman, pytest, or custom automation scriptsintermediate
- Experience integrating automated tests into CI/CD pipelines using tools like Selenium, JUnit, Jenkins, GitHub Actions, or similar.intermediate
- Knowledge of cloud platforms such as AWS, Azure, GCP.intermediate
- Understanding of data privacy, compliance, and governance frameworks.intermediate
- Knowledge of UI automated testing frameworks like Selenium, JUnit, TestNGintermediate
- Familiarity with monitoring/observability tools such as Datadog, Prometheus, or Cloud Watchintermediate
- Frameworks like Mocha/Jest and performance testing tools like JMeter or k6intermediate
- Excellent critical-thinking and problem-solving skillsintermediate
- Strong communication and collaboration skillsintermediate
- Demonstrated awareness of how to function in a team settingintermediate
- Demonstrated presentation skillsintermediate
Required Qualifications
- Master’s degree with 4 - 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree with 6 - 8 years of experience in Computer Science, IT or related field (experience)
- Certifications in Databricks, AWS, Azure, or data QA (e.g., ISTQB). (experience)
- AWS Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments) (experience)
Responsibilities
- Design, develop, and maintain automated test scripts for data pipelines, ETL jobs, and data integrations.
- Validate data accuracy, completeness, transformations, and integrity across multiple systems.
- Collaborate with data engineers to define test cases and establish data quality metrics.
- Develop reusable test automation frameworks and CI/CD integrations (e.g., Selenium, Jenkins, GitHub Actions).
- Perform performance and load testing for data systems.
- Maintain test data management and data mocking strategies.
- Identify and track data quality issues, ensuring timely resolution.
- Perform root cause analysis and drive corrective actions.
- Contribute to QA ceremonies (standups, planning, retrospectives) and drive continuous improvement in QA processes and culture.
- Build and automate end-to-end data pipeline validations across ingestion, transformation, and consumption layers using Databricks, Apache Spark, and AWS services such as S3, Glue, Athena, and Lake Formation.
Tags & Categories
Software Development Test Engineer
Amgen
Software Development Test Engineer
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain automated test scripts for data pipelines, ETL jobs, and data integrations.
- Validate data accuracy, completeness, transformations, and integrity across multiple systems.
- Collaborate with data engineers to define test cases and establish data quality metrics.
- Develop reusable test automation frameworks and CI/CD integrations (e.g., Selenium, Jenkins, GitHub Actions).
- Perform performance and load testing for data systems.
- Maintain test data management and data mocking strategies.
- Identify and track data quality issues, ensuring timely resolution.
- Perform root cause analysis and drive corrective actions.
- Contribute to QA ceremonies (standups, planning, retrospectives) and drive continuous improvement in QA processes and culture.
- Build and automate end-to-end data pipeline validations across ingestion, transformation, and consumption layers using Databricks, Apache Spark, and AWS services such as S3, Glue, Athena, and Lake Formation.
What we expect of you
- Master’s degree with 4 - 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree with 6 - 8 years of experience in Computer Science, IT or related field
- Certifications in Databricks, AWS, Azure, or data QA (e.g., ISTQB).
- AWS Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments)
Must-Have Skills
- Experience in QA roles, with strong exposure to data pipeline validation and ETL Testing.
- Hands-on expertise in front-end Node.js testing and performance testing of infrastructure components.
- Validate data accuracy, transformations, schema compliance, and completeness across systems using PySpark and SQL.
- Strong hands-on experience with Python, and optionally PySpark, for developing automated data validation scripts.
- Proven experience in validating ETL workflows, with a solid understanding of data transformation logic, schema comparison, and source-to-target mapping.
- Experience working with data integration and processing platforms like Databricks/Snowflake, AWS EMR, Redshift etc…
- Experience in manual and automated testing of data pipelines executions for both batch and real-time data pipelines.
- Perform performance testing of large-scale complex data engineering pipelines.
- Ability to troubleshoot data issues independently and collaborate with engineering teams for root cause analysis
- Strong understanding of QA methodologies, test planning, test case design, and defect lifecycle management.
- Hands-on experience with API testing using Postman, pytest, or custom automation scripts
- Experience integrating automated tests into CI/CD pipelines using tools like Selenium, JUnit, Jenkins, GitHub Actions, or similar.
- Knowledge of cloud platforms such as AWS, Azure, GCP.
- Understanding of data privacy, compliance, and governance frameworks.
- Knowledge of UI automated testing frameworks like Selenium, JUnit, TestNG
- Familiarity with monitoring/observability tools such as Datadog, Prometheus, or Cloud Watch
- Frameworks like Mocha/Jest and performance testing tools like JMeter or k6
- 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
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
20,000 - 40,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Experience in QA roles, with strong exposure to data pipeline validation and ETL Testing.intermediate
- Hands-on expertise in front-end Node.js testing and performance testing of infrastructure components.intermediate
- Validate data accuracy, transformations, schema compliance, and completeness across systems using PySpark and SQL.intermediate
- Strong hands-on experience with Python, and optionally PySpark, for developing automated data validation scripts.intermediate
- Proven experience in validating ETL workflows, with a solid understanding of data transformation logic, schema comparison, and source-to-target mapping.intermediate
- Experience working with data integration and processing platforms like Databricks/Snowflake, AWS EMR, Redshift etc…intermediate
- Experience in manual and automated testing of data pipelines executions for both batch and real-time data pipelines.intermediate
- Perform performance testing of large-scale complex data engineering pipelines.intermediate
- Ability to troubleshoot data issues independently and collaborate with engineering teams for root cause analysisintermediate
- Strong understanding of QA methodologies, test planning, test case design, and defect lifecycle management.intermediate
- Hands-on experience with API testing using Postman, pytest, or custom automation scriptsintermediate
- Experience integrating automated tests into CI/CD pipelines using tools like Selenium, JUnit, Jenkins, GitHub Actions, or similar.intermediate
- Knowledge of cloud platforms such as AWS, Azure, GCP.intermediate
- Understanding of data privacy, compliance, and governance frameworks.intermediate
- Knowledge of UI automated testing frameworks like Selenium, JUnit, TestNGintermediate
- Familiarity with monitoring/observability tools such as Datadog, Prometheus, or Cloud Watchintermediate
- Frameworks like Mocha/Jest and performance testing tools like JMeter or k6intermediate
- Excellent critical-thinking and problem-solving skillsintermediate
- Strong communication and collaboration skillsintermediate
- Demonstrated awareness of how to function in a team settingintermediate
- Demonstrated presentation skillsintermediate
Required Qualifications
- Master’s degree with 4 - 6 years of experience in Computer Science, IT or related field OR Bachelor’s degree with 6 - 8 years of experience in Computer Science, IT or related field (experience)
- Certifications in Databricks, AWS, Azure, or data QA (e.g., ISTQB). (experience)
- AWS Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments) (experience)
Responsibilities
- Design, develop, and maintain automated test scripts for data pipelines, ETL jobs, and data integrations.
- Validate data accuracy, completeness, transformations, and integrity across multiple systems.
- Collaborate with data engineers to define test cases and establish data quality metrics.
- Develop reusable test automation frameworks and CI/CD integrations (e.g., Selenium, Jenkins, GitHub Actions).
- Perform performance and load testing for data systems.
- Maintain test data management and data mocking strategies.
- Identify and track data quality issues, ensuring timely resolution.
- Perform root cause analysis and drive corrective actions.
- Contribute to QA ceremonies (standups, planning, retrospectives) and drive continuous improvement in QA processes and culture.
- Build and automate end-to-end data pipeline validations across ingestion, transformation, and consumption layers using Databricks, Apache Spark, and AWS services such as S3, Glue, Athena, and Lake Formation.
Tags & Categories