Requirements
Strong understanding of fundamental QA concepts and methodologies, including:
Test planning and execution
Defect lifecycle management
Regression and integration testing
SDLC/STLC processes
Hands-on experience with:
PySpark
Cloud platforms (AWS, Azure, or GCP)
Experience with Databricks or Spark-based platforms.
Role Overview
We are seeking a detail-oriented and highly motivated Senior QA Engineer with strong experience in PySpark, cloud technologies, and core QA engineering practices. The ideal candidate will be responsible for ensuring the quality, reliability, and performance of large-scale data and application platforms through comprehensive testing strategies and automation.
This role requires collaboration with developers, data engineers, product teams, and DevOps teams to establish and maintain high-quality engineering standards across cloud-based platforms and data processing systems.
Key Responsibilities
Design, develop, and execute comprehensive test plans, test cases, and test strategies.
Validate large-scale data pipelines and data transformations built using PySpark.
Perform functional, regression, integration, API, and end-to-end testing.
Develop and maintain automated test frameworks and scripts.
Validate data quality, integrity, completeness, and consistency across systems.
Work closely with engineering teams to identify, reproduce, and resolve defects.
Test cloud-native applications and distributed data platforms.
Participate in CI/CD processes and support automated quality gates.
Ensure adherence to QA standards, best practices, and release processes.
Contribute to performance, scalability, and reliability testing initiatives.
Mentor junior QA engineers and promote quality engineering culture.
Required Skills & Qualifications
Bachelor’s degree in Computer Science, Engineering, or related field.
5+ years of experience in Software QA / Quality Engineering.
Strong understanding of fundamental QA concepts and methodologies, including:
Test planning and execution
Defect lifecycle management
Regression and integration testing
SDLC/STLC processes
Hands-on experience with:
PySpark
Cloud platforms (AWS, Azure, or GCP)
Experience testing data engineering or big data applications.
Strong SQL skills and experience validating large datasets.
Experience with API testing tools such as Postman or REST Assured.
Familiarity with automation frameworks using Python, Java, or similar languages.
Experience with CI/CD tools such as Jenkins, GitHub Actions, or GitLab CI.
Strong analytical, debugging, and troubleshooting skills.
Excellent communication and collaboration abilities.
Preferred Qualifications
Experience with Databricks or Spark-based platforms.
Knowledge of data warehouse and ETL testing methodologies.
Experience with test automation frameworks like Selenium or PyTest.
Exposure to containerization technologies such as Docker and Kubernetes.
Understanding of Agile/Scrum methodologies.
Cloud certifications are a plus.
Strong attention to detail
Problem-solving and analytical thinking
Collaboration and teamwork
Ownership and accountability
Nice to Have
Experience with performance or load testing tools
Exposure to real-time data streaming systems
Knowledge of data governance and data validation frameworks
Experience in enterprise-scale environments
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