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Job Type: Permanent Work Model: Hybrid Reference code: 132605 Primary Location: Toronto, ON All Available Locations: Toronto, ON; Burlington, ON

What will your typical day look like?

The Data Quality Management Lead is responsible for designing, implementing, and operationalizing the enterprise Data Quality (DQ) framework across business domains. This role ensures that critical data assets are accurate, complete, consistent, timely, and fit-for-purpose to support regulatory reporting, analytics, operational excellence, and strategic decision-making.

Key Responsibilities

Data Quality Strategy, Framework and Implementation

• Develop, implement, operationalize and support the ongoing execution of the enterprise Data Quality Management framework.
• Establish data quality standards, procedures, and control mechanisms and translate into actionable controls and practices.
• Ensure alignment with enterprise Data Governance and Risk Management frameworks and integrate into the enterprise Data Architecture.

Data Quality Monitoring, Controls and Issues Management

• Design and implement automated DQ rules and validation checks across critical data elements (CDEs).
• Integrate automated agents for continuous monitoring and real-time issue flagging, audit trails, reporting and issue escalation and resolution.
• Implement issue management workflows with clear ownership and SLA tracking.
• Monitor recurring data quality issues and trends, escalating material risks as required through governance channels

Stakeholder Engagement & Domain and Data Stewards Enablement

• Partner with Data Owners and Data Stewards to embed DQ accountability.
• Facilitate domain-level DQ forums and working groups.
• Provide guidance to business units on DQ best practices and control design.

Develop and Implement DQ Management Standards Across all Relevant Data-Related Initiative

• Establish standards for data profiling, data requirements, data validation, reconciliation and other relevant testing for all initiatives that require implementation through the Enterprise Data Platform.
• Ensure all relevant initiatives include sufficient business testing and UAT will production today in advance of implementation with sufficient testing timelines to certify data quality in advance of go-live.
• Support identification and resolution of data-related issues pre and post go-live

Metadata & Data Controls Integration

• Collaborate with Data Governance, Metadata Management, and Data Architecture teams.
• Ensure DQ rules are aligned with data lineage and data classification frameworks.
• Support integration of DQ capabilities within data platforms (e.g., ETL, MDM, Data Lakes).
• Ensure that all data pipelines undertake a certification process to meet control and data quality standards and requirements

Reporting & Stakeholder Communication

• Prepare enterprise-level DQ dashboards including historical, current and projected DQ related KPIs.
• Present insights, trends, and risk exposures to senior leadership.
• Track remediation effectiveness and continuous improvement metrics.

Continuous Improvement

• Identify automation opportunities in DQ monitoring and remediation.
• Benchmark against industry best practices.
• Evaluate emerging technologies and innovations that enable Data Quality Management including Agentic AI, LLMs, autonomous agents, and AI tooling to uplift DQ maturity.

About the team

The Data & Insights team serves as the enterprise center of excellence dedicated to enabling trusted, timely, and actionable information across the organization. We partner closely with business and technology stakeholders to modernize reporting ecosystems, elevate analytics capabilities, and strengthen data management practices. Our mandate spans standardizing metrics, enhancing data quality and governance, advancing analytics and automation, and supporting scalable data platforms that drive informed decision-making. By combining technical expertise with strong governance discipline and business alignment, we help transform data into meaningful insights that improve performance, mitigate risk, and support strategic outcomes.

Enough about us, let's talk about you

You are someone with these required skills:

• Bachelor's degree in Information Systems, Data Management, Computer Science, or related field.
• 8+ years of experience in Data Governance, Data Quality, or Data Management.
• 3+ years leading enterprise-wide DQ initiatives.
• Demonstrated experience delivering Data Quality programs, Data governance frameworks, Data lifecycle management, and/or relevant controls frameworks
• Experience with leading DQ tools such as: Informatica DQ, Collibra DQ, Talend, Ataccama etc.
• Strong SQL skills and familiarity with data profiling, data analysis, DQ rule design, and DQ metrics.
• Demonstrated experience leveraging modern AI tools for data management.
• Hands-on use of agentic coding tools such as GitHub Copilot, Claude Code, OpenAI Codex etc as well as Cursor, VSC and/or equivalent. to accelerate DQ engineering, rule creation, data profiling, data analysis, metadata alignment, issue root-cause analysis and resolution, and/or documentation.
• Demonstrated experience in use AI agents for automated DQ rule generation, anomaly detection, and issue remediation.
• Experience integrating AI-assisted controls within ETL, MDM, and cloud platforms.

It would be great for you to have some of these nice to have's as well:

• Experience leading AI adoption initiatives across governance, analytics, or engineering teams.
• Knowledge of BCBS 239, GDPR, SOX, equivalent regulatory standards, and/or controls frameworks.
• Relevant vendor certifications, ISO 8000 experience
• Experience with Azure and Databricks, or equivalent hyperscaler technology
• Experience building AI agents for metadata enrichment or automated lineage extraction

Total Rewards

The salary range for this position is $85,000 - $156,000, and individuals may be eligible to participate in our bonus program. Deloitte is fair and competitive when it comes to the salaries of our people. We regularly benchmark across a variety of positions, industries, sectors, targets, and levels. Our approach is grounded on recognizing people's unique strengths and contributions and rewarding the value that they deliver.

Our Total Rewards Package extends well beyond traditional compensation and benefit programs and is designed to recognize employee contributions, encourage personal wellness, and support firm growth. Along with a competitive base salary and variable pay opportunities, we offer a wide array of initiatives that differentiate us as a people-first organization. On top of our regular paid vacation days, some examples include: $4,000 per year for mental health support benefits, a $1,300 flexible benefit spending account, firm-wide closures known as "Deloitte Days", dedicated days of for learning (known as Development and Innovation Days), flexible work arrangements and a hybrid work structure.