Senior Data Analyst, Servicing

About the role

This role centers on transforming raw loan servicing data into structured, actionable datasets. You will be responsible for designing and implementing data models that convert operational logs into reliable information. A key part of the work involves creating and documenting standardized data schemas to ensure consistency and accuracy across all servicing metrics.

A major component of this position is developing and maintaining scalable frameworks to track crucial performance indicators like delinquency trends, roll rates, and recovery outcomes. You will use tools like BigQuery SQL to build high-performance queries and design intuitive Tableau dashboards to visualize these metrics for management.

Success in this role means acting as the primary analytical support for the Servicing team. This involves translating complex operational workflows into logical data structures and delivering clear, actionable insights from the data. The goal is to drive measurable business impact within loan servicing, collections, or general operational analytics.

The salary for this role ranges from $110,000 to $150,000.

Skills that matter here

• SQL: You will use SQL, specifically BigQuery, to handle complex queries, joins, subqueries, and window functions to extract and transform servicing data.
• BigQuery: This role requires writing high-performance BigQuery SQL to build and maintain analytical frameworks and datasets.
• Tableau: You will design and build intuitive Tableau dashboards to visualize key servicing metrics and trends for various stakeholders.
• Python: Working knowledge of Python is expected for data manipulation tasks within the analytical workflow.

Who this role suits

• Someone with 3-5+ years of experience in data analysis, specifically within loan servicing, collections, or operational analytics.
• An individual who excels at translating complex operational processes into clear, structured data models and schemas.
• A person who can effectively communicate complex data trends and insights to non-technical stakeholders.
• A candidate with a proven track record in fintech or financial services environments.

From the employer

What You’ll Do

• Design and implement robust data models that transform raw servicing logs into structured, "gold-standard" datasets.
• Build and maintain scalable frameworks to monitor delinquency trends, roll rates, and recovery performance.
• Elevate our analytics toolkit by writing high-performance BigQuery SQL and designing intuitive Tableau dashboards.
• Create and document standardized data schemas that ensure a "single source of truth" for all servicing-related metrics.
• Act as the analytical backbone for the Servicing team, translating complex operational workflows into logical data structures.

What We Look For

• 3–5+ years of experience using data to drive measurable business impact, specifically within loan servicing, collections, or operational analytics.
• Deep proficiency in SQL (preferably BigQuery); expert at handling complex joins, subqueries, and window functions.
• Proven ability to design and maintain structured datasets and schemas.
• Experience building intuitive dashboards for managers.
• Strong understanding of servicing-specific metrics such as Net Charge-Offs (NCO) and recovery rates.
• Proven track record in a fintech or financial services environment.
• Working knowledge of Python for data manipulation.
• Appreciation for experimental design for A/B testing collections strategies.
• Clear ability to translate complex servicing trends into actionable insights.