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ZI
Data Quality Analyst – Data Governance, AI-Ready Data
Posted last month
About the role
- Data Quality Analyst monitoring and improving data quality across critical domains for Zurich Canada. Utilizing AI assisted techniques for data governance and compliance within a hybrid work model.
Responsibilities
- Implement and operate data quality controls (profiling, validation, reconciliation) in accordance with governance defined standards and thresholds.
- Measure and monitor data quality dimensions including accuracy, completeness, consistency, timeliness, and fitness for use.
- Produce data quality KPIs and metrics required for MR 5f governance reporting and dashboards.
- Apply AI assisted data quality capabilities (e.g., automated profiling, anomaly detection, rule generation) to improve coverage, efficiency, and early detection of data quality risks.
- Assess data readiness for analytics and AI use cases by identifying issues related to bias, data completeness, consistency, and semantic clarity.
- Partner with Data Stewards, Engineers, and Analytics teams to ensure data quality controls are embedded upstream in pipelines supporting AI and advanced analytics.
- Identify, document, and track data quality issues, including root cause analysis and remediation status.
- Provide evidence of data quality control operation to support audits, MR 5f risk reviews, and AI governance assessments.
- Escalate material data quality issues through defined governance channels; does not independently accept data or AI risk.
- Maintain operational metadata, data quality rules, and issue logs for assigned data domains.
- Support enrichment of metadata and lineage to improve data discoverability, explainability, and trust for analytics and AI consumption.
- Ensure data quality findings are traceable to systems, pipelines, and business definitions.
Requirements
- Bachelor’s degree in Data Management, Analytics, Computer Science, Information Systems, or a related field.
- 3–6 years of experience in data quality, data governance, analytics, or data management roles.
- Strong SQL and data analysis skills across large, complex datasets.
- Solid understanding of enterprise data quality concepts and control based operating models.
- Experience with AI assisted or automated data quality tools (e.g., automated profiling, anomaly detection, rule suggestion).
- Understanding of data preparation requirements for analytics and AI use cases.
- Familiarity with metadata management, data lineage, and data catalog practices.
- Ability to collaborate effectively with data engineering, analytics, and governance teams supporting AI initiatives.
Benefits
- Health insurance
- Competitive total compensation package
- Four weeks of vacation
- Four personal days
- Access to training and development opportunities
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