Title: Data Quality Management Lead

Location: Toronto, ON / Burlington, ON (Hybrid)

Duration: Permanent

Key Responsibilities

Data Quality Strategy & Framework

- Design, implement, and operationalize the enterprise Data Quality Management framework, standards, procedures, and controls.
- Translate DQ standards into actionable controls and practices across business domains and enterprise data platforms.
- Align Data Quality practices with enterprise Data Governance, Risk Management, and Data Architecture strategies.
- Establish scalable processes that improve overall Data Quality maturity across the organization.

Data Quality Monitoring, Controls & Issue Management

- Design and implement automated DQ rules, validation checks, profiling, reconciliation, and certification processes for Critical Data Elements (CDEs).
- Establish continuous monitoring and proactive issue detection across enterprise data pipelines.
- Implement issue management workflows with clear ownership, SLA tracking, escalation, root-cause analysis, and remediation.
- Identify recurring data quality trends and escalate material risks through appropriate governance channels.
- Ensure data pipelines and implementations meet required data quality and control standards prior to production release.

Stakeholder & Stewardship Leadership

- Partner with Data Owners and Data Stewards to establish clear accountability for data quality.
- Facilitate domain-level DQ forums and working groups and provide guidance on DQ best practices and control design.
- Collaborate with Data Governance, Metadata Management, Architecture,



and Engineering teams to align DQ rules with lineage, classification, and enterprise data standards.
- Support business testing, UAT, and pre- and post-production validation to ensure data integrity throughout delivery lifecycles.

Reporting, Automation & Continuous Improvement

- Develop enterprise DQ dashboards, KPIs, scorecards, and reporting for senior leadership.
- Track remediation effectiveness, trends, risk exposure, and continuous improvement metrics.
- Identify opportunities to automate DQ monitoring, profiling, anomaly detection, root-cause analysis, and remediation.
- Evaluate emerging technologies including Agentic AI, LLMs, autonomous agents, and AI-assisted development tools to improve Data Quality capabilities and operational efficiency.

Required Qualifications

- Bachelor's degree in Information Systems, Data Management, Computer Science, or a related field.
- 8+ years of experience in Data Quality, Data Governance, or Enterprise Data Management, including 3+ years leading enterprise-wide DQ initiatives.
- Demonstrated experience designing, implementing, and operationalizing enterprise Data Quality frameworks, controls, and programs.
- Experience with enterprise DQ platforms such as Informatica Data Quality, Collibra DQ, Talend,



Ataccama, or equivalent.
- Strong SQL skills and experience with data profiling, reconciliation, data analysis, DQ rule design, and DQ metrics.
- Experience embedding Data Quality controls into ETL/ELT, MDM, cloud, data lake, and up-to-date enterprise data platforms.
- Strong understanding of metadata management, data lineage, data classification, Critical Data Elements, and lifecycle management.
- Demonstrated experience using modern AI tools to accelerate Data Quality engineering, rule creation, profiling, issue analysis, remediation, and documentation.
- Experience with AI-assisted development tools such as GitHub Copilot, Claude Code, OpenAI Codex, Cursor, VS Code, or similar tools.
- Experience applying AI agents to automated DQ rule generation, anomaly detection, issue analysis, or remediation.
- Solid stakeholder management skills with the ability to work across business, governance, architecture, engineering, risk, and leadership teams.

Preferred Qualifications

- Experience with Azure and Databricks or comparable cloud/lakehouse technologies.
- Knowledge of BCBS 239, GDPR, SOX, ISO 8000, or similar regulatory and controls frameworks.
- Experience leading AI adoption initiatives across Data Governance, Data Management, Analytics, or Engineering functions.
- Experience building or using AI agents for metadata enrichment, automated lineage extraction, or intelligent Data Quality automation.
- Relevant Data Quality, Data Management, cloud, Informatica, or Databricks certifications.

📌 Data Quality Manager (Toronto)
🏢 Motion Recruitment
📍 Toronto