Senior Analyst, Data Foundations and Infrastructure

BMO•Toronto, ON

1d•CA$56,000 - CA$103,500•Onsite

About The Position

The 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.

Requirements

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)
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

Nice To Haves

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
Background in banking, finance, or revenue data environments preferred

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.