Data Quality Analyst - Data Governance; AI- Data

Data Quality Analyst - Data Governance (AI-Ready Data) The Opportunity

Are you looking for a caring, collaborative, values-driven workplace with inspiring teammates and leaders? Do you have the ambition and desire to be the best and thrive at the most impactful global insurance provider in the world? Look no further than Zurich Canada.

If you have experience in executing data quality controls, and are looking for a new challenge, we would love to hear from you.

Zurich Canada is currently looking for a Data Quality Analyst – Data Governance (AI-Ready Data) to monitor, measure, and improve data quality across critical data domains, leveraging both traditional and AI assisted data quality techniques to provide transparency, evidence, and insight for governance oversight, regulatory compliance, and responsible AI adoption.

Reporting to the Data Platform Leader, you will execute data quality controls required under the enterprise Data Governance Framework and MR5f Data Management Risk Policy, with a specific focus on ensuring data is AI ready, trusted, and fit for advanced analytics and AI use cases.

This is a unique opportunity to build your knowledge and experience for the future in a supportive environment where your voice matters. This posting is a new vacancy.

Zurich Canada uses artificial intelligence–enabled tools to support certain aspects of the recruitment process, including the initial review and screening of applications. Artificial intelligence is not the sole basis for candidate shortlisting or selection. All hiring decisions are reviewed and made by qualified hiring professionals.

Zurich follows a hybrid work model requiring three days per week of in-person presence, which may include time in the office or market-facing engagements.

Data Quality Control Execution (MR5f)

• 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 MR5f governance reporting and dashboards.

AI Rady Data & Advanced Quality Techniques

• 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.

Issue Management, Evidence & Risk Escalation

• Identify, document, and track data quality issues, including root cause analysis and remediation status.
• Provide evidence of data quality control operation to support audits, MR5f risk reviews, and AI governance assessments.
• Escalate material data quality issues through defined governance channels; does not independently accept data or AI risk.

Metadata, Lineage & Transparency

• 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.

Required:

• 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.

Preferred:

• 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…