Job Role - Quality Assurance Engineer III

About the Role

We are seeking a meticulous and analytical Data-Intensive Quality Assurance Engineer to join our team. The successful candidate will play a critical role in ensuring the quality and reliability of our data-driven products and services. This position requires a deep understanding of data processes and technical proficiency to perform comprehensive automated and manual testing.

Key Responsibilities

Design and execute test plans for data pipelines, data models, and transformation logic across Redshift and Snowflake environments. Validate data accuracy, completeness, and consistency in large-scale distributed systems. Write advanced SQL queries to verify data integrity across source systems, staging layers, and analytics outputs. Collaborate with data engineers to test ETL/ELT workflows and monitor scheduled jobs. Identify, document, and triage data quality issues and inconsistencies. Automate data validation checks and UI validation using tools such as Selenium and Python. Perform manual and automated testing, regression, and integration testing for analytics dashboards and data products. Validate KPIs and metrics in BI platforms by comparing them with underlying data. Contribute to building reusable data QA assets and continuous testing frameworks.

Requirements Must-Have:

3+ years of QA or data validation experience, with a strong focus on data-centric systems. Proficiency in SQL, with hands-on experience writing complex queries in many databases (Amazon Redshift, Snowflake, MySQL, PostgreSQL, etc.). Understanding of data warehousing concepts, ELT/ETL processes, and dimensional modeling. Strong programming skills in Python or similar scripting languages for test automation. Excellent problem-solving skills and attention to detail when dealing with large datasets. Experience in manual and automated testing of BI dashboards and validating metrics against raw and transformed data. Familiarity with CI/CD practices and tools Excellent communication skills to collaborate effectively with technical and non-technical stakeholders. Exposure to cloud data platforms and services (AWS, Redshift, Aurora).

Nice-to-Have:

Familiarity with version control systems (e.g., Git) and CI/CD pipelines. Knowledge of data governance, lineage, and metadata management tools. Experience with performance and load testing tools (e.g., JMeter). Knowledge of Agile methodologies and practices.