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Senior Data Quality & Governance Engineer, Finance Data Steward
Four Seasons
Toronto, CAN
Full Time
Senior
Hybrid
1 weeks ago
Data GovernanceData Quality ManagementMicrosoft D365 Finance & OperationsOne StreamLongviewKyriba
Free
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Data GovernanceData Quality ManagementMicrosoft D365 Finance & Operations
About the Role
Four Seasons is seeking a Senior Data Quality & Governance Engineer, Finance Data Steward to safeguard the accuracy, quality, and security of financial data across enterprise systems.
Key Skills for This Role
Data GovernanceData Quality ManagementMicrosoft D365 Finance & OperationsOne StreamLongviewKyriba
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 for FDM, Procurement, and other Finance procedures
- Monitor FDM, Procurement, and other Finance related data across data quality dimensions using Hub provided tools and scorecards with Informatica
- Log, triage, and coordinate the remediation of data quality issues, escalating to the Hub when enterprise wide action is required
- Maintain the FDM and other master and reference data for Finance (e.g., chart of accounts, vendors, products) in accordance with FS 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 that may impact AI and advanced analytics initiatives
- 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 D365, One Stream, and data lake reporting and integrations meet data governance expectations
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
- Excellent collaboration and communication skills
- Analytical mindset with ability to translate data into actionable insights
- Familiarity with how financial data supports AI, machine learning, generative AI, and intelligent automation
- Experience using AI enabled productivity, analysis, or governance tools
- Understanding of concepts including AI agents, RAG, semantic models, enterprise knowledge management, and responsible AI
- Strong working knowledge of Microsoft D365 Finance & Operations and Longview (or equivalent)
Full Job Posting
About the role
- The Senior Data Quality & Governance Engineer, Finance Data Steward 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 including Microsoft
- Reporting to the Manager, Data Governance Enablement, the role is also responsible for ensuring finance data is AI ready across the enterprise platforms.
- The role operationalizes data governance to enable consistent, high quality, and well contextualized data that supports advanced analytics and AI use cases.
What You’ll Be Doing
- Implement data governance policies, standards, and controls issued by the Hub within Finance applications and workflows, and other data stores.
- Translate hub defined expectations into data dictionaries, reference data standards and lineage requirements for FDM, Procurement, and other Finance specific procedures and documentation.
- Monitor FDM, Procurement, and other Finance related data across data quality dimensions using Hub provided tools and scorecards with Informatica.
- Partner with Data quality team to Log, triage, and coordinate the remediation of data quality issues, escalating to the Hub when enterprise wide action is required.
- Maintain the FDM and other master and reference data for Finance (e.g., chart of accounts and related hierarchies, vendors, products) in accordance with FS 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 required to support reporting, analytics, AI agents, copilots, and intelligent automation solutions.
- Leverage AI enabled capabilities to accelerate metadata documentation, data classification, issue investigation, root cause analysis, stewardship reporting, and knowledge management activities.
- Partner with product teams, AI solution teams, and data engineering teams to identify and remediate data readiness gaps that may impact AI and advanced analytics initiatives.
- Identify opportunities where AI agents, copilots, and automation capabilities can improve Finance data governance processes and user access to trusted data.
- Serve as the primary contact for Product Leads and other stakeholders on data governance matters, representing Key Finance data in forums and working groups.
- Partner with Product Leads, IT, and Data Engineering to ensure that D365, One Stream, and data lake reporting and integrations meets data governance expectations.
What You Bring
- 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 would be an asset.
- Excellent collaboration and communication skills to bridge enterprise and departmental needs.
- 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 to improve efficiency and decision making.
- Understanding of concepts including AI agents, retrieval augmented generation (RAG), semantic models, enterprise knowledge management, and responsible AI.
Financial Systems Expertise
- Strong working knowledge of Microsoft D365 Finance & Operations and Longview (or equivalent planning/consolidation tools).
- Familiarity with general ledger structures, chart of accounts, cost center hierarchies, and financial reporting workflows.
Data Governance & Quality Tools
- Hands on experience with data catalog / metadata management platforms (e.g., Collibra, Alation, Informatica EDC) to maintain lineage, definitions, and business glossary entries.
- Ability to configure and monitor data quality rules and scorecards using enterprise DQ tools (e.g., Informatica Data Quality, Talend, Ataccama, or equivalent).
Data Analysis & Querying
- Proficiency in SQL or other query languages to profile and validate data across ERP, data lake, and BI environments.
- Comfort with data profiling techniques and root cause analysis to identify and remediate quality issues.
Integration & Architecture Awareness
- Understanding of enterprise data lake concepts, ETL/ELT pipelines, and how Finance data flows from source systems to analytics platforms.
- Familiarity with APIs, data transformations, and security controls that support regulated financial data.
Reporting & Visualization
- Ability to design dashboards and visualizations in tools such as Power BI or Tableau to communicate data quality metrics and trends.
- Experience generating clear, audit ready reports for Finance leadership and compliance teams.
Data Management Frameworks & Compliance
- Working knowledge of industry standards such as DAMA DMBOK or EDM Council practices.
- Awareness of SOX, GDPR, and other financial data privacy/security regulations relevant to a global finance function.
AI Readiness
- 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.
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