Data Analyst

Must be local to Dallas, Texas and available for in-person interviews and onsite work in Downtown Dallas.

About the Role

The Data Analyst will be part of the Strategic & Risk Management team, supporting the development of a robust data and analytics framework for monitoring market, liquidity, credit, and operational risks. This role is ideal for a data-focused analyst who can work with large datasets, understand business requirements, develop data pipelines, and translate complex data into actionable insights for business and risk stakeholders. The successful candidate will work closely with experienced Risk SMEs and cross-functional teams to understand business processes, identify data requirements, analyze trends and exposures, and improve reporting and automation.

Responsibilities

• Analyze large and complex datasets to identify trends, patterns, anomalies, risk indicators, and opportunities for process improvement.
• Gather and translate business requirements into data requirements, analytical solutions, dashboards, and reporting outputs.
• Independently map business processes, workflows, data sources, and data pipelines to understand end-to-end data flow.
• Design and develop data models and databases using Microsoft Fabric and related data technologies.
• Build, maintain, and optimize ETL/ELT pipelines to integrate data from multiple sources.
• Use SQL, Python, Excel/VBA, and other analytical tools to manipulate, validate, transform, and analyze data.
• Develop automated reports, dashboards, and visualizations using tools such as Tableau.
• Monitor and analyze operational, market, liquidity, and credit risk data across business units.
• Analyze counterparty credit exposure, trends, exceptions, and other key risk metrics.
• Perform data quality checks and maintain accurate records to support risk reporting for business leaders and executive management.
• Collaborate with Risk SMEs, business stakeholders, technology teams, and other cross-functional groups to resolve complex data and reporting issues.
• Identify opportunities to automate manual reporting processes and improve data accuracy, efficiency, and scalability.
• Investigate discrepancies and unexpected results by understanding the underlying data, business logic, and system processes rather than relying solely on predefined procedures.
• Document data sources, business rules, transformation logic, workflows, and analytical processes.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Finance, Economics, Data Analytics, Information Systems, or a related field.
• Experience in Data Analysis, Business Intelligence, Risk Analytics, Financial Analytics, or a related analytical role.
• Strong experience working with large datasets and performing data manipulation, cleansing, validation, and analysis.
• Strong SQL skills with the ability to write complex queries and analyze relational data.
• Experience with Python for data analysis, transformation, and automation.
• Strong proficiency in Microsoft Excel, including advanced formulas, PivotTables, data analysis, and preferably VBA.
• Experience creating dashboards and data visualizations using Tableau or similar BI tools.
• Strong analytical and problem-solving skills with the ability to independently investigate ambiguous data and business problems.
• Ability to understand business requirements and translate them into data models, pipelines, reports, and analytical solutions.
• Excellent attention to detail and ability to identify data inconsistencies and anomalies.
• Must be authorized to work for any employer in the United States without requiring sponsorship.

Preferred Qualifications

• Experience working within a financial institution, banking, investment, trading, or risk management environment.
• Knowledge of financial markets, fixed income products, trading lifecycle, counterparty risk, credit risk, or liquidity risk.
• Experience with Microsoft Fabric, including Fabric Data Factory, Lakehouse, Warehouse, or related components.
• Experience designing databases, data models, and end-to-end ETL/ELT pipelines.
• Experience with data engineering concepts and cloud-based data platforms.
• Familiarity with risk management frameworks, controls, and enterprise reporting.
• Experience automating recurring reporting and analytical processes.
• Strong communication skills with the ability to work effectively with both technical teams and business/Risk SMEs.