Senior Analyst, Data Foundations and Infrastructure

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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 experience in data analytics, data operations, or financial data roles
• Experience working with large, complex datasets and operational processes
• Background in banking, finance, or revenue data environments preferred

The above represents BMO Financial Group’s pay range and type.

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group’s expected target for the first year in this position.

BMO Financial Group’s total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: