Senior Data Quality Analyst

About the Role

Sr. Analyst, Data QA is a role within the data organization to ensure the accuracy, reliability, and trustworthiness of data in the analytics data platform. The position sits at the intersection of data engineering, analytics engineering, and quality automation, and works under the guidance of the Analytics Data Platform Manager. You will be responsible for building and automating robust data quality checks, testing frameworks, and observability systems to ensure data pipelines function correctly and that downstream analytics are reliable.

You will collaborate with data engineers, analytics engineers, and platform stakeholders to identify data quality risks, implement automated validation processes, and ensure that Snowflake- and dbt-based data models meet high standards of consistency and accuracy.

If you have a passion for clean, trusted data — and enjoy building systems that ensure data quality at scale — this is the role for you.

• Design, build, and maintain a comprehensive suite of automated data quality checks in dbt (tests, exposures, docs) and custom Python validation layers for all gold-layer models and key business metrics.

• Implement and maintain dbt tests, custom Python-based validations, and Monte Carlo monitors to ensure complete coverage of critical datasets and metrics.

• Serve as the first responder and owner for data-quality incidents; perform rapid triage, coordinate fixes with pipeline owners, and ensure resolution within agreed SLAs.

• Collaborate with analytics engineers during model development to embed testable contracts (schema, uniqueness, freshness, referential integrity, business-rule validation) from day one.

• Partner with data engineers to validate end-to-end pipeline correctness, including reconciliation between source systems, staging, and gold layers.

• Continuously expand coverage of critical metrics and entities (user, content, engagement, subscription, revenue, etc.) so that data consumers can trust the semantic layer without manual verification.

• Build and maintain internal dashboards and runbooks that make data-quality health transparent to leadership and the broader analytics organization.

• Proactively identify systemic data risks (drift, schema changes, upstream breaks) and propose architectural or process improvements to prevent recurrence.

• 5+ years of hands-on experience in data quality, analytics engineering, or data engineering with a heavy emphasis on testing and validation.

• Advanced proficiency in dbt — writing and maintaining dbt tests, macros, and documentation for data validation.

• Strong Snowflake expertise: querying, performance troubleshooting, understanding of micro-partitions, clustering, and zero-copy cloning in a data-quality context.

• Advanced Python skills for building custom data-validation frameworks, integrating with APIs, and automating alerts/workflow (pandas, Great Expectations or similar is a plus).

• Experience with data quality and observability tools such as Monte Carlo, Soda, Great Expectations, or similar frameworks.

• Working knowledge of data modeling principles (dimensional and entity modeling) and data lifecycle management.

• Ability to debug complex data issues across ingestion, transformation, and semantic layers.

• Familiarity with version control (Git), CI/CD, and modern data orchestration tools (Airflow, Dagster, Prefect, etc.).

• Strong collaboration skills and the ability to work across engineering, analytics, and product teams to uphold data quality standards.

• Excellent communication skills — able to translate technical data quality findings into clear insights for stakeholders.

• Knowledge of data lineage tools or metadata management systems.

• Prior experience in media, content analytics, or digital publishing.

• Exposure to data privacy, compliance, or security monitoring (GDPR, CCPA, etc.).

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