Senior Analyst, Data Foundations and Infrastructure at BMO in Toronto, ON

Job description

Application Deadline: 10/30/2026 Address: 250 Yonge Street Job Family Group: Data Analytics & Reporting About the Opportunity: This is an exciting opportunity to join Commercial Banking's Data Foundations & Infrastructure team and play a key role in maintaining the accuracy, quality, and integrity of the data that powers reporting, analytics, and business decision-making across Commercial Banking. The team sits at the intersection of business and technology, supporting foundational Commercial Banking datasets across Canada and the U.S. The successful candidate will combine financial acumen, strong SQL capabilities, and data analysis skills to investigate data discrepancies, perform reconciliations, improve operational processes, and support ongoing enhancements to Commercial Banking's data environment. This role is ideal for someone who enjoys solving complex data problems, understanding the business meaning behind data, and contributing to process improvements that drive operational efficiency. It offers excellent exposure to Commercial Banking data, financial systems, data infrastructure, and broader Data & Analytics initiatives across the organization. BMO Commercial Bank Data Foundations team is focusing on revenue data reconciliation, processing, and operational data workflows. The role involves executing and improving recurring processes, ensuring data integrity, and supporting finance and business stakeholders. Core Responsibilities Executes and supports end-to-end revenue data processes, including monthly/periodic reconciliation, investigation, and adjustment workflows across multiple data sources. Performs data reconciliation between financial systems and internal data platforms, identifying variances, root causes, and required adjustments to ensure accuracy and completeness. Writes and maintains SQL queries and Python scripts to extract, transform, validate, and reconcile data across systems. Supports the execution, monitoring, and troubleshooting of recurring data pipelines and batch processes, ensuring timely delivery of revenue datasets. Works closely with finance and business teams to understand data discrepancies, validate outputs, and resolve issues in a timely manner. Identifies opportunities to automate and streamline manual reconciliation and reporting processes using SQL, Python, and other workflow tools. Develops and maintains operational reporting and dashboards to track reconciliation status, data quality, and process performance. Documents data flows, reconciliation logic, and operational procedures to support transparency, auditability, and onboarding. Ensures adherence to data quality standards (accuracy, completeness, consistency, timeliness) within revenue datasets. Collaborates with data engineers and analytics teams to enhance data models and improve processing efficiency. Provides ongoing support to business stakeholders by investigating data issues and responding to inquiries related to revenue data. Qualifications Must-have Skills Strong hands-on experience with SQL (required) – ability to query, join, and troubleshoot large datasets Experience with Python (required) for data processing, automation, or scripting Working knowledge of data reconciliation, data validation, or data quality processes Basic understanding of accounting principles (e.g., revenue, GL, reconciliations) Good-to-have Experience supporting financial or revenue data processes in banking or financial services Familiarity with ETL/data pipelines Experience with data quality frameworks or controls Exposure to month-end or periodic processing cycles Core Competencies Foundational: Data reconciliation & validation Data operations & process execution SQL & data manipulation Systems thinking Problem solving Intermediate: Process improvement & automation Stakeholder communication (finance + business) Analytical thinking & root cause analysis Documentation & operational governance Experience Typically, 1–3 years of relevant experienc