Quality Assurance (QA Engineer)

About the Role Mattel is seeking a QA Engineer focused on consumer data quality to validate identity, profile, behavioral, and transactional data across the data platform. The role involves end-to-end testing of data pipelines, reconciling metrics against upstream sources, and ensuring accuracy of dashboards and analytics releases. You will collaborate with data engineers and analytics teams to improve QA standards and automation.

What You'll Do

• Validate consumer identity, profile, behavioral, and transactional data across all layers of the data platform, from source systems through curated analytics datasets.
• Perform end-to-end dataset comparisons to ensure consumer-level records, counts, and attributes remain consistent as data moves through ingestion and transformation layers.
• Reconcile consumer metrics and KPIs on Tableau and ThoughtSpot dashboards with upstream source and warehouse tables.
• Test ETL/ELT pipelines that ingest and transform consumer data, including events, purchases, interactions, and engagement signals.
• Validate business logic related to consumer attribution, segmentation, and aggregation rules used in analytics.
• Conduct row-level, aggregate-level, and trend-based testing to identify issues such as duplication, data loss, mis-joins, or incorrect consumer rollups.
• Partner with data engineers and analytics teams to investigate and resolve data quality issues.
• Create reusable test plans, validation checklists, and reconciliation queries for consumer analytics datasets.
• Document test cases, validation results, and QA approvals for consumer data releases.
• Participate in Agile ceremonies, including sprint planning, backlog refinement, and retrospectives.
• Contribute to the continuous improvement of consumer data QA standards, validation frameworks, and automation coverage.

What We're Looking For

• 2–5 years of QA experience focused on consumer data, analytics, or business intelligence.
• Strong proficiency in SQL for validating consumer-level datasets (e.g., Google BigQuery or similar platforms).
• 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.
• Strong 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.

Compensation & Benefits Not disclosed.