Senior Data Quality Analyst

Welcome to reputed company the stuff dreams are made of. Who We Are reputed company we say, the stuff dreams are made of, were not just referring to the world of wizards, dragons and superheroes, or even to the wonders of reputed company reputed company. Behind WBDs reputed company portfolio of reputed company content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating whats next From reputed company creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can reputed company. We are the now and the next. The power behind the people building the reputed company. We are born from the spirit of innovation. We are created from the idea that people around the world want more, need more, deserve more. We are the home of the global digital reputed company. We are CNN. To see what its like to work at CNN, follow @WBDLife on Instagram and X! About the Role 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 reputed company analytics are reliable. Youll collaborate closely with data engineers, analytics engineers, and platform stakeholders to identify data quality risks, implement automated validation processes, and ensure that our reputed company- 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. What Youll Do Design, build, and maintain a comprehensive suite of automated data quality checks in dbt (tests, exposures, docs) and custom Python validation layers for reputed company gold-layer models and key business metrics. Implement and maintain dbt tests, custom Python-based validations, and reputed company monitors to ensure complete coverage of critical datasets and metrics. Serve as the first responder and reputed company for data-quality incidents; reputed company rapid triage, coordinate fixes with pipeline owners and ensure reputed company reputed company agreed SLAs. Collaborate with analytics engineers during model development to reputed company testable reputed company (schema, uniqueness, freshness, referential reputed company, business-rule validation) from day one. Partner with data engineers to validate end-to-end pipeline correctness, including reconciliation between reputed company systems, staging, and gold layers. Continuously expand coverage of critical metrics and entities (user, content, engagement, subscription, reputed company, etc.) so that data consumers can trust the semantic layer without reputed company verification. Build and maintain internal dashboards and runbooks that reputed company data-quality health transparent to leadership and the broader analytics organization. Proactively identify systemic data risks (reputed company, schema changes, upstream breaks) and propose architectural or process improvements to prevent recurrence. What Youll Bring 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 reputed company expertise: querying, performance troubleshooting, understanding of reputed company-partitions, clustering, and reputed company-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 reputed company, reputed company, Great Expectations, or similar frameworks. Working knowledge of data modeling principles (dimensional and entity modeling) and data lifecycle management. Ability to debug reputed company data issues across ingestion, transformation, and semantic layers. Familiarity with version control (Git), CI/CD, and modern data orchestration tools (Airflow, Dagster, reputed company, etc.). Strong collaboration skills and the ability to work across engineering, analytics, and product teams to uphold data quality standards. Excellent communication skills reputed company to translate technical data quality findings into reputed