Quality Assurance Engineer

Responsibilities:

• Test Planning & Design: Develop and execute comprehensive test plans for data pipelines, data warehouses, and data analysis processes, including functional, regression, performance, and load testing.
• Data Quality Assurance: Establish and enforce data quality standards, ensuring data accuracy, consistency, completeness, and timeliness throughout the data lifecycle.
• Test Automation: Develop and maintain automated test scripts to streamline testing processes, increase test coverage, and ensure faster feedback loops.
• Defect Management: Identify, document, and track defects, working with the development team to resolve issues and ensure timely fixes.
• Data Analysis & Reporting: Conduct data analysis to identify potential quality issues and trends, providing insights and recommendations for improvement.
• Collaboration & Communication: Collaborate closely with the Product Owner, data engineers, and other stakeholders to ensure clear communication, alignment on test requirements, and effective issue resolution.
• Continuous Improvement: Champion best practices for data quality and testing, promoting a culture of quality within the data team.

Qualifications:

• Proven experience as a QA Engineer in a data-focused environment, with a strong understanding of data warehousing and data pipelines.
• Proficiency in SQL for data validation and testing.
• Experience with testing tools and methodologies, including unit testing, integration testing, functional testing, performance testing, and load testing.
• Experience with data quality analysis and validation techniques.
• Strong understanding of data lifecycle management, data governance, and data security principles.
• Excellent communication, problem-solving, and analytical skills.
• Ability to work independently and as part of a team, with a proactive and results-oriented mindset.

Bonus Points:

• Experience with cloud-based data platforms (e.g., Azure, AWS, GCP) and associated testing tools.
• Experience with scripting languages like Python for data analysis and automation.
• Experience with data visualization tools for data analysis and reporting.
• Experience with data integration tools like ODI, Informatica, or Databricks.