Data QA Engineer III

Preferred Hotels & Resorts is a company that values independence and individuality in the hospitality industry. They are seeking a Data QA Engineer III who will design and execute automated tests, implement data quality monitoring, and perform manual verifications to ensure the accuracy and reliability of data engineering deliverables.

Responsibilities:

• Collaborate with the engineering team to refine work requests in an agile development system, translating requirements into testable data quality criteria
• Develop comprehensive test plans and test cases as part of project planning, including test strategy for validation, reconciliation, regression, and monitoring
• Provide accurate estimates of effort and duration of QA tasks
• Create and maintain automated tests to validate pipeline requirements, transformation logic, and downstream analytics/report outputs
• Write and optimize SQL queries for automated validations (such as row counts, uniqueness, referential integrity, reconciliation, business-rule checks, etc.)
• Build regression suites for critical datasets and dashboards to ensure consistent numbers across releases and backfills
• Create and maintain deterministic test datasets (fixtures) and “golden” expected results for repeatable validation
• Assist with the verification and recreation of user-reported data issues, including data lineage/traceback from report to source
• File detailed and actionable defect reports, including reproduction steps, expected outcomes, and evidence (queries, sample records, screenshots of report values when relevant)
• Work collaboratively with engineers to troubleshoot defects, validate fixes, and prevent recurrence via new tests and monitoring
• Continuously improve QA processes, frameworks, and tools for data testing and validation to align with best practices
• Integrate test automation with deployment automation, work tracking, and test tracking systems to enforce automated quality gates
• Schedule and manage automated test runs (PR/CI, nightly, and post-deploy), ensuring consistent and reliable execution
• Implement data observability checks and alerting for freshness, volume, distribution/anomaly detection, and schema drift; tune alert thresholds to reduce noise
• Collect, consolidate, and analyze test and monitoring results to identify trends, systemic issues, and opportunities to improve data reliability
• Define and develop key performance indicators (KPIs) for measuring test effectiveness (coverage, escaped defects, time-to-detection, time-to-resolution)
• Manage and prioritize work using the ticketing system while maintaining regular communication in stand-ups and stakeholder meetings
• Conduct code reviews of test code, SQL validation logic, and monitoring rules to ensure adherence to best practices and high-quality deliverables
• Partner with Data Engineering and BI stakeholders to validate semantic models and report logic (e.g., dataset/model measures, transformations, refresh behavior)
• Contribute to technical documentation of processes, tools, workflows, and standards
• Mentor other team members by sharing knowledge, conducting training sessions, and providing guidance on best practices for data testing and quality
• Take ownership of complex or high-impact initiatives (e.g., establishing regression strategy, monitoring standards), ensuring timely delivery and alignment with business objectives

Requirements:

• Bachelor's degree in Computer Science, Information Systems /other relevant degree or equivalent professional experience
• Demonstrated experience writing automated QA tests, especially for back-end systems without UI
• Expert knowledge of relevant languages, such as SQL, Python, JavaScript, and/or C#
• Expert knowledge of test automation tools such as Cypress, Great Expectations
• Excellent analytical and problem-solving skills with a high level of attention to detail
• Strong communication and collaboration skills to work effectively with cross-functional teams
• Experience with CI/CD pipelines and version control systems (e.g., Jenkins, Git)
• Knowledge of business intelligence tools such as Power BI or Tableau
• Understanding of Agile development processes