Director of Data Analytics- Finance & Regulatory Data Management

Scotiabank•Toronto, ON

4d•Onsite

About The Position

Scotiabank is seeking a senior leader to establish and scale Finance Data Management capabilities across the enterprise. The Director of Finance Data Management will define, govern, and operationalize the financial and regulatory data ecosystem that underpins planning, reporting, regulatory compliance, and advanced analytics. This role is critical to enabling a single source of truth for financial and regulatory data, improving data quality, and ensuring consistent, auditable data flows across Finance, Treasury, Risk, and regulatory reporting functions. The successful candidate will combine data governance, finance and regulatory domain expertise, and emerging AI capabilities to position data as a strategic asset that drives decision-making, automation, and regulatory confidence.

Requirements

10+ years of experience in data management, data governance, or finance data architecture
Strong domain expertise in financial reporting and FP&A
Strong domain expertise in regulatory reporting (capital, liquidity, stress testing, etc.)
Proven experience implementing enterprise data governance frameworks
Experience supporting regulatory, audit, and compliance-driven data requirements
Finance & regulatory data models (GL, hierarchies, regulatory attributes)
Data governance, stewardship, and control frameworks
Data quality management and reconciliation processes
Metadata, lineage, and master data management
Integrated Finance–Risk data ecosystems
Modern data platforms (e.g., cloud data lakes, Spark, ETL/ELT pipelines)
Data governance and catalog tools
Exposure to AI/analytics tools and techniques (e.g., predictive modeling, anomaly detection, automation)
Experience with financial systems (SAP, Oracle, Anaplan, Workiva, Cognos)
Reporting tools (Power BI, Tableau)
Strong executive presence with ability to influence CFO, CRO, CIO, and regulators/audit stakeholders
Ability to translate complex data challenges into business and regulatory outcomes
Experience leading cross-functional teams in matrixed environments

Nice To Haves

Experience in financial services or regulated industries
Deep familiarity with BCBS 239 or equivalent regulatory data frameworks
Experience in finance transformation or regulatory remediation programs
Exposure to AI adoption in Finance or Risk environments

Responsibilities

Define and lead the Finance Data Management strategy, incorporating regulatory data requirements across capital, liquidity, and financial reporting.
Align with enterprise data strategy to support integrated Finance, Risk, and Regulatory reporting.
Establish a roadmap for data modernization and AI-enabled data capabilities.
Establish and enforce data governance frameworks across financial and regulatory data, including data ownership and stewardship models, standard data definitions, hierarchies, and taxonomies, and policies for data quality, lineage, and controls.
Ensure strong alignment with BCBS 239 principles and internal governance standards.
Build and operationalize data quality and control frameworks that support financial reporting accuracy and regulatory reporting integrity and timeliness.
Implement processes for reconciliation, validation, and issue management, and root cause analysis and remediation of data issues.
Partner with Finance, Risk, and Compliance to meet regulatory and audit expectations.
Oversee data readiness and integration for regulatory reporting (e.g., capital, liquidity, stress testing).
Ensure traceability from source systems to regulatory submissions, with full auditability.
Support Finance and Risk teams in adapting to evolving regulatory requirements.
Oversee end-to-end financial and regulatory data flows, ensuring seamless integration across GL, sub-ledgers, and transaction systems, FP&A and planning platforms, and Risk, Treasury, Capital, and Liquidity systems.
Partner with engineering teams to implement modern data platforms (e.g., cloud, Spark-based pipelines).
Enable the use of AI and advanced analytics on governed finance data, including predictive insights (forecasting, anomaly detection), data quality automation and exception identification, intelligent reconciliation and matching, and natural language querying and reporting.
Ensure AI models are built on trusted, governed, and explainable data foundations.
Partner with Data Science and Analytics teams to operationalize AI-driven Finance use cases.
Implement and maintain data lineage and metadata management across finance and regulatory data flows.
Ensure full transparency and explainability of key financial and regulatory metrics.
Support audit, compliance, and regulatory reviews with strong traceability.
Serve as a bridge between Finance, Risk, and Technology stakeholders.
Standardize financial and regulatory data models across reporting and planning processes.
Improve usability and accessibility of data for executive reporting, regulatory submissions, and scenario analysis and planning.
Build and lead teams across data governance and stewardship, data quality and controls, and regulatory data management.
Establish a scalable operating model across Finance and Risk domains.
Drive accountability for data ownership within business functions.

Benefits

A fully implemented Finance and Regulatory data governance framework
High-quality, trusted, and audit-ready financial and regulatory data
Reduced reconciliation effort and fewer regulatory data issues
Transparent, traceable data flows across Finance and Risk systems
AI-enabled capabilities improving insight, efficiency, and control
Strong alignment across Finance, Risk, and Technology on data usage
Play a critical role in shaping data as a strategic asset across Finance and Risk
Directly impact regulatory confidence, audit outcomes, and enterprise decision-making
Lead the integration of data governance and AI-driven innovation
Partner with senior leadership on high-visibility transformation initiatives