Quality Assurance Engineer

Photon • seattle, wa, us • 1m ago

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.

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