Data Quality Analyst

Job Description

Role Summary: The Data Quality Analyst will ensure robust monitoring and validation of cybersecurity metrics across all three levels of data quality control as defined in the DUSE framework. This includes rule creation, data profiling, exception handling, and supporting remediation actions across ~400+ existing DQ rules. Key Responsibilities: Support completion of DUSE 4 tasks by identifying and evidencing relevant DQ controls. Define, test, and implement DQ rules for cyber metrics (timeliness, accuracy, completeness). Profile data flows and monitor feeds against DQ thresholds using platforms like Databricks and Aurora . Log data quality exceptions, assess severity, and support remediation through root cause analysis. Maintain and update DQ monitoring dashboards and track compliance with internal and regulatory milestones. Work closely with Cyber SME and Data Engineers for validation and alignment on cyber-specific controls and issues. Own triage and gap validation for cyber risk metrics, ensuring closure of monitoring gaps. Collaborate with Data Architect and Engineers to build scalable DQ rules aligned with legacy and emerging cyber data structures. Supervise creation of control evidencing and closure packs for DUSE audits. Guide the team in identifying recurring issues and logging DQ exceptions. Facilitate training and ramp-up of junior analysts; serve as SME for DQ remediation approaches. Skillsets Required: Strong background in data quality assessment , issue management, and rule-based monitoring. Deep understanding of data management in financial services , especially for regulatory metrics. Experience working with large-scale DQ frameworks across DUSE/DMOV controls . Strong skills in issue resolution, DQ rule design , and control validation. Hands-on experience with tools like Databricks , Aurora , Excel , and Confluence . Able to lead process improvements and maintain high-quality documentation. Experience in BCBS 239/DUSE compliance, ideally within cybersecurity or operational risk. Familiarity with exception management, DQ scorecards, and automated reporting workflows. Excellent stakeholder collaboration skills, especially with ITSO and process owners.