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We are seeking a detail-oriented and analytical Commercial Banking Data Analyst to support large-scale North American commercial banking data operations. The ideal candidate will have strong experience in data analysis, data validation, reporting, and business intelligence using Python, SAS, SQL, and Power BI. This role will focus on ensuring data accuracy, improving operational reporting, supporting compliance initiatives, and delivering actionable business insights across commercial banking systems.
Key Responsibilities:
Analyze, validate, and monitor large-scale North American Commercial Banking datasets involving customer, account, and business banking information across multiple databases.
Perform data extraction, transformation, reconciliation, and exception analysis using Python, SAS, and SQL for commercial banking reporting and operational processes.
Develop, maintain, and enhance data quality and validation rules to identify missing, duplicate, inconsistent, or high-risk records within commercial banking data environments.
Write, optimize, and troubleshoot complex SQL queries involving joins, aggregations, filtering, and data validation across multiple commercial banking database tables.
Support data governance, compliance, audit, and reporting initiatives by monitoring data accuracy, consistency, and operational standards within commercial banking systems.
Design and develop interactive Power BI dashboards, visual reports, and operational scorecards to present trends, exceptions, KPIs, and business insights to stakeholders and management teams.
Collaborate with business users, data teams, and technical stakeholders to improve reporting processes, automate workflows, and enhance overall data management efficiency.
Assist in identifying process improvement opportunities and implementing automation solutions for recurring operational and reporting tasks.
Document data validation procedures, reporting logic, and operational workflows to support governance and compliance requirements.
Participate in troubleshooting data discrepancies and resolving data-related operational issues.
Required Skills & Qualifications:
Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Finance, or related field.
Strong experience with SQL for data querying, analysis, and validation.
Hands-on experience with Python and/or SAS for data processing and reconciliation activities.
Experience working with commercial banking, financial services, or enterprise-scale operational datasets.
Proficiency in Power BI for dashboard development and data visualization.
Strong understanding of data quality management, data governance, and reporting processes.
Experience with relational databases and large-volume data environments.
Excellent analytical, problem-solving, and communication skills.
Ability to work collaboratively with cross-functional business and technical teams.
Preferred Qualifications:
Experience in North American Commercial Banking operations or financial data environments.
Knowledge of banking compliance, regulatory reporting, and operational risk management.
Familiarity with ETL processes and data reconciliation methodologies.
Experience with automation and workflow optimization initiatives.
