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About the Role

The Finance Data Steward, Senior Data Quality & Governance Engineer provides an end-to-end data stewardship service that safeguards the accuracy, quality, and security of all data related to the financial data model and other finance data across key finance and enterprise systems. This role is responsible for ensuring finance data is AI-ready across enterprise platforms and operationalizes data governance to enable consistent, high-quality, and well-contextualized data that supports advanced analytics and AI use cases.

Responsibilities

Implement data governance policies, standards, and controls within Finance applications and workflows.
Translate hub-defined expectations into data dictionaries, reference data standards, and lineage requirements.
Monitor Finance related data across data quality dimensions using provided tools and scorecards.
Log, triage, and coordinate the remediation of data quality issues.
Maintain master and reference data for Finance in accordance with Data Governance guidelines.
Validate new or changed data to ensure compliance before it is shared across the organization.
Ensure Finance data is trusted, consistently defined, well-documented, and enriched with metadata, lineage, quality indicators, and business context.
Leverage AI-enabled capabilities to accelerate metadata documentation, data classification, issue investigation, root-cause analysis, stewardship reporting, and knowledge management.
Partner with product teams, AI solution teams, and data engineering teams to identify and remediate data readiness gaps.
Identify opportunities where AI agents, copilots, and automation capabilities can improve Finance data governance processes.
Serve as the primary contact for Product Leads and other stakeholders on data governance matters.
Partner with Product Leads, IT, and Data Engineering to ensure reporting and integrations meet data governance expectations.
Educate impacted staff on enterprise Data Governance policies and promote best practices.
Provide feedback to the Hub on practical challenges, helping refine enterprise standards.

Requirements

Bachelor’s degree in Finance, Accounting, or Data Management (CPA or MBA an asset).
3–5 years of experience in Finance operations, procurement, data governance, or related roles.
Familiarity with One stream, Microsoft D365, and enterprise data lake environments.
Proven experience in data governance, with a focus on metadata, reference data, and master data management.
Strong understanding of data quality concepts and data management frameworks (e.g., DAMA).
Strong understanding of finance related regulations and compliance requirements.
Familiarity with Informatica or similar data governance or data management technologies.
Excellent collaboration and communication skills.
Analytical mindset with the ability to translate data into actionable insights.
Familiarity with how financial data supports AI, machine learning, generative AI, and intelligent automation use cases.
Experience using AI-enabled productivity, analysis, or governance tools.
Understanding of concepts including AI agents, retrieval-augmented generation (RAG), semantic models, enterprise knowledge management, and responsible AI.
Experience developing prompts, automations, or AI-assisted workflows is considered an asset.
Strong working knowledge of Microsoft D365 Finance & Operations and Longview.
Familiarity with general ledger structures, chart of accounts, cost-center hierarchies, and financial reporting workflows.
Hands-on experience with data catalog / metadata management platforms.
Ability to configure and monitor data quality rules and scorecards using enterprise DQ tools.
Proficiency in SQL or other query languages.
Comfort with data profiling techniques and root-cause analysis.
Understanding of enterprise data lake concepts, ETL/ELT pipelines.
Familiarity with APIs, data transformations, and security controls.
Ability to design dashboards and visualizations in tools such as Power BI or Tableau.
Experience generating clear, audit-ready reports.
Working knowledge of industry standards such as DAMA-DMBOK or EDM Council practices.
Awareness of SOX, GDPR, and other financial data privacy/security regulations.
Understanding of data requirements for generative AI, AI agents, and intelligent automation solutions.
Familiarity with Microsoft Copilot, Copilot Studio, Azure AI services, and enterprise AI governance concepts.
Understanding of semantic models, knowledge management, metadata enrichment, and retrieval techniques used by AI systems.
Ability to assess data assets for AI readiness and recommend improvements.
Experience using AI-enabled tools to improve stewardship productivity and governance effectiveness.
Demonstrates high personal and professional integrity.
Takes ownership of finance data quality and governance tasks.
Is results-oriented and committed to delivering accurate, trusted data on time.
Maintains composure and effectiveness when priorities shift or issues arise.
Understands finance operations and reporting needs.
Navigates ambiguity and complex data flows.
Manages multiple data quality initiatives and remediation activities.
Applies strong process management skills to implement repeatable, auditable controls.
Comfortable working in an agile environment.
Listens carefully to Finance stakeholders and clarifies data requirements.
Prepares clear, concise documentation and scorecards.
Uses visual storytelling—dashboards, process maps, and presentations—to highlight key insights.
Organizes and monitors large volumes of financial data with exceptional attention to detail.
Establishes clear expectations and timelines for data quality checks, issue remediation, and reporting.
Handles multiple demands by re-prioritizing quickly.
Consistently delivers on data governance commitments.
Proactively clarifies performance expectations for Finance data owners.
Provides colleagues with the guidance and tools needed to improve data quality.
Acknowledges and celebrates contributions from Finance and IT partners.
Builds trust and credibility across Finance teams.
Works productively in a matrix environment.
Serves as the primary liaison between the Finance spoke and the Data Governance Hub.
Actively seeks input and ideas from Finance, IT, and enterprise stakeholders.

Skills

Microsoft D365
One Stream
Longview
Kyriba
Enterprise Data Lake
Informatica
SQL
Power BI
Tableau
DAMA-DMBOK
EDM Council practices
SOX
GDPR
Microsoft Copilot
Copilot Studio
Azure AI services
AI agents
Retrieval-augmented generation (RAG)
Semantic models
Enterprise knowledge management
Responsible AI

Location

Toronto, Ontario

Work Type

Hybrid

Experience Level

Senior

Education Level

Bachelor’s degree

Salary/Compensations

$85,000 - $125,000 CAD

About the Company

Four Seasons is a global, luxury hotel management company managing over 130 hotels and resorts and 50 private residences in 47 countries.
Central to Four Seasons employee experience and social impact programming is the company’s commitment to supporting cancer research, and the advancement of diversity, inclusion, equality and belonging.

Equal Opportunity

Four Seasons is committed to providing employment accommodation in accordance with the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act. If contacted for an employment opportunity, please advise Human Resources if you require accommodation.