Senior Data Quality Analyst

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We are looking for a Sr. Analyst, Data QA to join our growing data organization and ensure the accuracy, reliability, and trustworthiness of data in our analytics data platform. This role sits at the intersection of data engineering, analytics engineering, and quality automation, working 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 are functioning correctly and that downstream analytics are reliable.

You’ll collaborate closely with data engineers, analytics engineers, and platform stakeholders to identify data quality risks, implement automated validation processes, and ensure that our Snowflake- and dbt-based data models meet the highest 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…