Data Quality Analyst

Role Overview: You will support the implementation and monitoring of data quality controls across global data products and shared data platforms. Your focus will be on accomplishing data validation checks, monitoring data health, and resolving data quality issues to ensure that data is accurate, complete, and reliable for downstream analytics and reporting. You will work closely with senior team members, data engineers, and business collaborators to support data quality monitoring, documentation, and governance activities across enterprise data platforms. Key Responsibilities: - Implement data validation and data quality rules using SQL to assess data completeness, accuracy, and consistency. - Develop scripts and reusable queries to support data validation, monitoring, and routine data quality checks. - Support the development and maintenance of Power BI dashboards, benchmark scorecards, and monitoring reports to track data quality performance, issue backlogs, SLA alignment, and data health trends. - Assist collaborators in accessing and interpreting data quality metrics and reports. - Investigate data quality issues across source systems, ingestion pipelines, and transformation layers. - Collaborate with data engineering and upstream system teams to support issue resolution and validation of fixes. - Maintain documentation for data quality rules, validation logic, important metric definitions, and monitoring processes. - Support metadata tagging, taxonomy, and ontology alignment to enable AI-ready data products while collaborating with Data Product Managers, Market CoE squads, and central Data & Governance teams. Qualifications Required: - Education: Quantitative bachelors degree or equivalent experience in Engineering, Statistics, Applied Math, Computer Science, Data Science, Economics, or related field. - Experience in data quality, data analytics, data management, or related data roles. - Strong SQL skills including joins, aggregations, and query optimization. - Basic to intermediate experience with Python for scripting, automation, or data processing. - Strong documentation, communication, and collaboration skills. Additional Details: When you bring together unexpected teams in the same room, you ignite bold thinking with the power to encourage life-changing medicines. In-person working gives you the platform needed to connect, work at pace, and challenge perceptions. AstraZeneca values inclusivity, diversity, and equality of opportunity, welcoming applications from all qualified candidates, regardless of their characteristics. Role Overview: You will support the implementation and monitoring of data quality controls across global data products and shared data platforms. Your focus will be on accomplishing data validation checks, monitoring data health, and resolving data quality issues to ensure that data is accurate, complete, and reliable for downstream analytics and reporting. You will work closely with senior team members, data engineers, and business collaborators to support data quality monitoring, documentation, and governance activities across enterprise data platforms. Key Responsibilities: - Implement data validation and data quality rules using SQL to assess data completeness, accuracy, and consistency. - Develop scripts and reusable queries to support data validation, monitoring, and routine data quality checks. - Support the development and maintenance of Power BI dashboards, benchmark scorecards, and monitoring reports to track data quality performance, issue backlogs, SLA alignment, and data health trends. - Assist collaborators in accessing and interpreting data quality metrics and reports. - Investigate data quality issues across source systems, ingestion pipelines, and transformation layers. - Collaborate with data engineering and upstream system teams to support issue resolution and validation of fixes. - Maintain documentation for data quality rules, validation logic, important metric definitions, and monitoring processes. - Support metadata tagging, taxonomy, and ontology alignment to enable AI-ready data products while collaborating with Data Product Managers, Market CoE squads, and central Data & Governance teams. Qualifications Required: - Education: Quantitative bachelors degree or equivalent experience in Engineering, Statistics, Applied Math, Computer Science, Data Science, Economics, or related field. - Experience in data quality, data analytics, data management, or related data roles. - Strong SQL skills including joins, aggregations, and query optimization. - Basic to intermediate experience with Python for scripting, automation, or data processing. - Strong documentation, communication, and collaboration skills. Additional Details: When you bring together unexpected teams in the same room, you ignite bold thinking with the power to encourage life-changing medicines. In-person working gives you the platform needed to connect, work at pace, and challenge perception