Data Quality Engineer

Key Responsibilities

Data Discrepancy Resolution

• Investigate the source of impressions missmatch 
• Assess input data stream quality that could cause measurement result to look wrong and provide guidance to customer on how to correct data capture
• Collaborate with Integrations, Engineering, and Customer Success teams to drive consistent, timely resolutions.

Monitoring & Quality Assurance

• Maintain catalog of platforms/publishers/formats system can or can not measure
• Design and maintain dashboards to monitor incoming data feeds and detect anomalies early.
• Define and implement alerting rules to flag gaps in coverage, delayed feeds, or unusual patterns.
• Partner with data engineers to automate validation checks across S2S integrations and pixel-based measurement systems.

Cross-Functional Enablement

• Serve as the primary escalation point for Support and Commercial teams on data quality questions.
• Create and maintain internal documentation and troubleshooting playbooks.
• Contribute to process automation that improves reliability and reduces manual intervention.

Basic Qualifications

• 3+ years of experience in Quality Assurance of data operations, or analytics engineering.
• Strong analytical skills, including proficiency with SQL/PySpark/Scala/R
• Clear, structured communication skills across technical and non-technical audiences.
• Proven ability to triage and resolve complex issues independently.

Preferred Qualifications

• Experience in digital advertising, media measurement, or marketing data platforms.
• Familiarity with AWS or GCC data stack (S3, Glue, Athena) or similar environments.
• Exposure to observability and monitoring frameworks (e.g., Grafana).
• Solid understanding of data pipelines, APIs, and log-based debugging.

Desired personal characteristics

• Detail-Oriented: They are precise, accurate, and have high standards for quality.
• Systematic: They prefer to follow established rules, procedures, and clear processes.
• Stable & Patient: They are even-tempered, calm, and dependable.

Supportive: They are often good listeners and work reliably to support a team, though they may be more task-focused than people-focused.