About Costco Wholesale: Costco Wholesale is a multi-billion-dollar global retailer with warehouse club operations in eleven countries. They provide a wide selection of quality merchandise, plus the convenience of specialty departments and exclusive member services, all designed to make shopping a pleasurable experience for their members. About Costco Wholesale India: At Costco Wholesale India, we foster a collaborative space, working to support Costco Wholesale in developing innovative solutions that improve members’ experiences and make employees’ jobs easier. Our employees play a key role in driving and delivering innovation to establish IT as a core competitive advantage for Costco Wholesale. The QE is responsible for ensuring the quality, accuracy, reliability, and performance of enterprise data products and analytics solutions through software engineering, automated data validation, and continuous testing. This role focuses on developing reusable test automation frameworks, validating end-to-end data pipelines, integrating automated testing into CI/CD processes, and leveraging AI technologies to improve testing efficiency and effectiveness. At Costco, we are on a mission to significantly leverage data to provide better products and services for our members. The QE works collaboratively with Product Owners, Data Architects, Data Engineers, Analytics Engineers, and DevOps teams to embed quality throughout the software development lifecycle, accelerate delivery, reduce manual testing efforts, and ensure high-quality data and analytics solutions. Job Duties/Essential Functions:
• Test Strategy & Automation: Defines and implements automation-first testing strategies for BI, Data Warehousing, ETL/ELT pipelines, and Data Analytics platforms, ensuring comprehensive functional and nonfunctional test coverage.
• Test Automation Framework Development: Designs, develops, and maintains scalable, reusable, and data-driven test automation frameworks using Python, SQL, PyTest, DataComPy, and other automation tools to accelerate testing and reduce manual efforts.
• Data Quality & Validation: Develops automated solutions to validate data accuracy, completeness, consistency, integrity, business rules, schema changes, and source-to-target reconciliation across enterprise data platforms.
• Data Pipeline & Integration Testing: Engineers and executes automated tests for end-to-end data pipelines, including data ingestion, transformation, incremental loads, Change Data Capture (CDC), data integration, and downstream consumption.
• BI & Analytics Testing: Develops automated validation solutions for BI reports, dashboards, semantic models, KPIs, aggregations, and business metrics to ensure reporting accuracy, data consistency,



and alignment with business requirements.
• CI/CD & Shift-Left Testing: Collaborates with Data Engineers and DevOps teams to integrate automated tests into CI/CD pipelines, implement automated quality gates, and promote shift-left testing practices for early defect detection and continuous quality assurance.
• Performance & Nonfunctional Testing: Designs, develops, and executes automated performance, scalability, reliability, and regression tests to validate data processing efficiency, query performance, pipeline throughput, and system stability.
• Google AI & AI-Driven Test Automation: Leverages Google AI technologies, including Gemini and Vertex AI, to enhance automated test case generation, SQL validation, test script development, intelligent data quality checks, anomaly detection, and root cause analysis while following enterprise AI governance and security standards.
• Defect Analysis & Quality Metrics: Investigates automated test failures, performs root cause analysis, documents and manages defects, and develops quality dashboards to monitor automation coverage, defect trends, data quality, test execution results, and release readiness.
• Engineering Excellence & Collaboration: Partners with Product Owners, Data Architects, Data Engineers, DevOps, and the Quality Engineering Center of Excellence (QE CoE) to establish automation standards, develop reusable engineering solutions, conduct code reviews, mentor engineers, and drive continuous quality improvement.
• Communicates technical concepts, testing outcomes, quality risks, and automation results effectively to technical and nontechnical stakeholders.
• Regular and reliable workplace attendance at the assigned location. Ability to operate vehicles, equipment or machinery:
• Computer, phone, printer, copier, fax Non-Essential Functions:
• Assists in other areas of the department as necessary.
• Assists in other areas of the company as necessary. Ability to operate vehicles, equipment or machinery:
• Same as Essential Functions Experience, Skills, Education & Licenses/Certifications: Required:
• 5+ years of experience in Software Quality Engineering, Software Development Engineering in Test (SDET), or test automation, preferably within BI, Data Engineering, or Data Analytics environments.




• Strong programming and automation development experience using Python and SQL, including designing reusable, maintainable, and scalable test automation solutions.
• Hands-on experience developing and maintaining automated testing frameworks
• Experience validating ETL/ELT pipelines, data transformations, data warehouses, data integration processes, and source-to-target data reconciliation.
• Solid understanding of data quality validation, including completeness, accuracy, consistency, uniqueness, referential integrity, and business rule verification.
• Experience developing automated functional, integration, regression, and end-to-end testing solutions for data-driven applications.
• Experience integrating automated test suites into CI/CD pipelines and implementing shift-left testing practices.
• Working knowledge of BI reporting, analytical data models, SQL queries, and KPI validation.
• Experience developing test strategies, automation plans, test scenarios, and automated validation solutions in an Agile development environment.
• Strong analytical, debugging, problem-solving, and root cause analysis skills, with the ability to collaborate effectively with cross-functional engineering teams. Recommended:
• BA/BS in Computer Science, Software Engineering, Information Systems, or equivalent software engineering experience.
• Experience with cloud data platforms such as Azure, Google Cloud Platform (GCP), Databricks, Snowflake, or equivalent technologies.
• Experience with Google AI technologies, including Gemini, Vertex AI, and AI-assisted development tools, to improve test automation, data validation, and quality engineering productivity.
• Experience with BI and analytics solutions such as Power BI, including semantic models, DAX calculations, KPI validation, and report testing.
• Experience designing and implementing metadata-driven, data-driven, and reusable automation frameworks.
• Knowledge of performance engineering, SQL query optimization, data pipeline performance testing, and workload scalability.
• Familiarity with data governance, data lineage, data contracts, and enterprise data quality standards.
• Exposure to AI-driven testing, intelligent anomaly detection, automated test generation, and emerging quality engineering technologies.
• Experience with retail industry data, enterprise analytics, or large-scale data processing platforms.
• Excellent verbal and written communication skills, with the ability to explain technical concepts to both technical and nontechnical audiences.
• Successful internal candidates will have spent one year or more on their current team.

📌 Quality Engineer- Data Analytics (Hyderabad)
🏢 Costco IT
📍 Hyderabad