Mattel
Toy and family entertainment
QualityAssurance(QAEngineer)
“Quality Assurance (QA Engineer) at Mattel. Skills: Consumer data, SQL, Data pipelines, BI tools. Validate consumer data across all layers. Perform end-to-end dataset comparisons”
Industry & Context.
Analytical and troubleshooting skills; Investigate and resolve consumer data quality issues
What They're Looking For.
Must Have
2–5 years of QA experience focused on consumer data, analytics, or business intelligence, Proficiency in SQL for validating consumer-level datasets, Hands-on experience testing data pipelines within modern data lake or warehouse architectures, Proven experience validating consumer-focused dashboards and metrics in BI tools, Solid understanding of consumer data models, including identity resolution, event data, and transactional facts, Experience using defect tracking and test management tools such as JIRA, Analytical and troubleshooting skills with exceptional attention to detail, Ability to clearly communicate consumer data issues to both technical and business stakeholders
Nice to Have
Experience testing analytics in Tableau, ThoughtSpot, Looker, or similar BI platforms, Familiarity with consumer identity, event tracking, and behavioral data models, Exposure to data quality frameworks, reconciliation processes, or automated data validation, Experience with CI/CD pipelines and version control (e. g. , Git) in analytics environments, Understanding of consumer data governance, lineage, and metadata practices, Knowledge of privacy and compliance considerations related to consumer data (GDPR, CCPA), Experience with automation tools such as Postman, Pytest, or SQL-based testing frameworks
What You'll Do.
Validate consumer data across all layers
Perform end-to-end dataset comparisons
Reconcile consumer metrics and KPIs
Test ETL/ELT pipelines
Validate business logic
and trend-based testing
Investigate and resolve data quality issues
Create reusable test plans
Document test cases and results
Participate in Agile ceremonies
How You'll Work.
Team & Collaboration
Partner with data engineers and analytics teams; Communicate data issues to stakeholders; Participate in sprint planning; Participate in backlog refinement; Participate in retrospectives
Communication Scope
Clearly communicate consumer data issues to both technical and business stakeholders
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