Data QA Engineer III

Preferred Travel Group

12d•$100,000 - $130,000•Remote

About The Position

We are seeking an experienced, analytical, detail‑oriented, and quality‑driven Data QA Engineer III. The Data QA Engineer will design and execute automated tests, implement data quality monitoring and alerting, and perform targeted manual verifications to ensure the accuracy, reliability, and trustworthiness of data engineering deliverables. This role focuses on validating data pipelines, transformations, data models, and BI/report outputs (not UI test automation) across ETL/ELT pipelines, reports, dashboards, and data management applications—so the business can confidently use data to drive decisions. Under the general supervision of the Manager of QA, the QA Data Engineer works closely with developers in the Data Engineering team.

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.

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.