Data Quality Analyst

Data Quality Analyst - Cupertino, CA (hybrid)

Role Summary

The QA Analyst will support the offers reporting project by ensuring high quality data, robust DQ reporting, and accurate validation across CDF and reporting tools. This role partners closely with geo/WW stakeholders and data teams to define data quality rules, analyze data quality, and follow up/validate data fixes.

Key Responsibilities

Define and govern data quality standards

Define and maintain data quality rules, data dictionaries, and business glossaries aligned to business requirements.

Participate in data governance and geo alignment discussions to align on definitions, standards, and controls.

Implement and monitor data quality controls

Partner with Data Quality and AiDP teams to design, implement, test, and monitor automated data quality checks in Offers CDF

Maintain Tableau based QA dashboards to monitor data quality anomalies, trends, and resolution times.

Validate and analyze data

Perform data profiling, reconciliation, and validation using SQL to ensure completeness, consistency, and accuracy across sourcing, enrichment, and reporting layers.

Triage and investigate data quality issues in Offers CDF to identify root causes across source, processing, and reporting layers, and coordinate resolution with appropriate teams.

Manage and resolve data issues

Work with geo teams and external vendors to investigate region specific data issues and drive resolution through working sessions and follow ups.

Track and create DQ issues or tickets raised with vendors (via Trello or email) and internal teams (via radar) and ensure timely remediation and closure.

Required Skills & Qualifications

Strong experience in data analysis, data validation, and QA activities within data-driven environments.

Proficiency in SQL to query, analyze, and validate data supporting reporting dashboards and business insights.

Proficiency in Tableau for dashboard creation and data visualization.

Experience working with data quality frameworks, rules definition, and remediation processes.

Excellent verbal and written communication skills, with the ability to clearly present data insights and collaborate effectively with technical and business teams.

Ability to work cross-functionally with global stakeholders and adapt to complex data environments.