Senior Data Analyst - Remote

About the role

The role serves as a senior analytics partner to the Credit team, providing data-driven insights across credit risk, portfolio performance, underwriting, and loss mitigation. The position requires a strong background in data analytics, specifically with credit-related data.

Responsibilities

• Serve as a senior analytics partner to the Credit team, providing data-driven insights across credit risk, portfolio performance, underwriting, and loss mitigation
• Develop, optimize, and maintain complex SQL queries to extract, transform, and analyze large volumes of financial and credit data from enterprise data warehouses
• Leverage Python (e.g., pandas, NumPy) to perform advanced data analysis, automation, validation, and feature engineering, complementing SQL-based workflows and improving analytical efficiency
• Create and maintain SSRS reports to support operational, regulatory, and management reporting needs, ensuring accuracy, consistency, and timeliness
• Analyze credit metrics such as delinquency, roll rates, charge-offs, recoveries, exposure, and vintage performance to identify trends, risks, and opportunities
• Partner closely with Credit Risk, Underwriting, Finance, and Compliance teams to ensure reporting aligns with business rules, policies, and regulatory expectations
• Validate data integrity and reconcile results across multiple systems to ensure reporting accuracy and reliability
• Translate complex analytical findings into clear, concise insights and recommendations tailored to both technical and non-technical audiences
• Mentor junior analysts and contribute to the development of best practices for SQL, reporting standards, and analytical methodologies
• Ensure adherence to data governance, security, and regulatory requirements specific to banking and credit data

Requirements

• 5+ years of experience in data analytics, with direct experience supporting Credit, Credit Risk, or Lending teams within a bank or financial services organization
• Solid working knowledge of credit concepts, including delinquency, charge-offs, recoveries, exposure, vintages, utilization, and portfolio performance
• Advanced Python proficiency, with hands-on experience using libraries such as pandas, NumPy, and related analytical packages for data manipulation, validation, automation, and large-scale analysis; experience building reusable scripts, analytical frameworks, or pipelines is highly valued
• Expert-level proficiency in SQL, with experience writing complex joins, CTEs, subqueries, window functions, and performance-optimized queries against large datasets
• Advanced experience with Power BI, including data modeling, DAX, custom measures, and designing executive-ready dashboards
• Advanced Excel skills, including pivot tables, Power Query, Power Pivot, complex formulas, and statistical or financial analysis
• Hands-on experience developing, maintaining, and supporting SSRS reports in a production environment
• Prominent analytical and problem-solving skills, with the ability to independently investigate issues and deliver insights
• Excellent communication skills with the ability to explain complex data findings to both technical and non-technical stakeholders

Preferred Qualifications

• Experience working with consumer or commercial lending products (e.g., credit cards, auto loans, personal loans, mortgages, or commercial loans)
• Experience working with large enterprise data warehouses and financial systems
• Experience building automated or self-service reporting solutions for business users
• Leverage Python for automation, process optimization, or advanced analytics (e.g., trend analysis, scenario analysis, or custom performance monitoring)
• Experience developing, validating, or deploying analytical or statistical models using Python, including feature engineering, model evaluation, and performance monitoring in a financial or risk analytics context
• Support senior leadership with executive-level reporting and insights
• Familiarity with banking regulatory and risk frameworks (e.g., CECL, stress testing, portfolio monitoring, audit support)
• Demonstrated ability to mentor junior analysts and establish analytics best practices