Senior Credit Analytics Engineer

Aqua Finance, Inc.; United States; On-site; USD 120,000 - 170,000; Full time

Job summary

Aqua Finance, Inc. is seeking a Senior Credit Analytics Engineer to build and maintain the analytical data foundation for underwriting, pricing, originations, collections, and portfolio analytics.

You will design analytics-ready datasets and reusable SQL assets to enable rapid, informed decision-making and scalable analytics. Collaborating with Data Engineering, IT, and Operations, you will translate Lending data into trusted assets, leveraging SQL, Python, dbt, Git, Snowflake, and BI tools

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, Economics, or related quantitative field, or commensurate work experience.
• 5 years of experience in analytics engineering, data engineering, business intelligence, or advanced analytics.
• Advanced SQL proficiency and experience with Python, dbt, and Git.

Responsibilities

• Design, develop, and maintain analytics-ready datasets supporting underwriting, credit risk, pricing, originations, collections, dealer oversight, performance, and portfolio analytics.
• Own and evolve the analytical data layer by developing reusable SQL objects, dimensional models, data marts, and curated datasets.
• Build and maintain scalable data transformation workflows using SQL, Python, dbt, Git, and Snowflake-native scheduling.

Skills

Advanced SQL

Python

dbt

Git

Education

Bachelor’s degree in a quantitative field

Tools

Snowflake

Power BI

Sigma

Job description

The Senior Credit Analytics Engineer is responsible for building and maintaining the analytical data foundation that supports underwriting, pricing, originations, collections, performance, and portfolio analytics. Embedded within the Credit Strategy & Analytics team, this role develops analytics-ready datasets, reusable SQL assets, scalable data models, and reporting solutions that enable analysts and business leaders to move quickly and make informed decisions.

Working closely with Data Engineering, IT, and Operations, the Senior Analytics Engineer simplifies and integrates data across the lending platform. Leveraging SQL, Python, dbt, Git, Snowflake, and business intelligence tools, this role translates complex lending data into trusted, well-documented, and easy-to-use analytical assets while balancing rapid delivery with long-term quality, maintainability, and scalability.

Essential Functions

• Design, develop, and maintain analytics-ready datasets supporting underwriting, credit risk, pricing, originations, collections, dealer oversight, performance, and portfolio analytics.
• Own and evolve the analytical data layer by developing reusable SQL objects, dimensional models, data marts, and curated datasets that enable consistent analysis and decision-making.
• Build and maintain scalable, reliable data transformation workflows using SQL, Python, dbt, Git, and Snowflake-native scheduling and automation capabilities.
• Simplify, consolidate, integrate, and reconcile data from lending and operational systems into trusted, business-facing analytical data products.
• Partner with analysts and business stakeholders to convert recurring or one-time analytical needs into reusable data products and standardized business logic that reduce duplicate effort and accelerate time-to-insight.
• Establish and promote reporting and business intelligence best practices, including consistent metrics, reusable datasets, semantic models, documentation, organization, and quality controls.
• Develop, maintain, and share ownership of reporting solutions using Power BI, Sigma, or comparable tools while enabling analysts to build and support reports efficiently.
• Design analytical solutions that balance speed, flexibility, performance, maintainability, and alignment with longer-term data architecture.
• Collaborate with the Data Engineering, IT, and Operations to align analytical solutions with enterprise standards, system capabilities, and business processes.
• Implement automated testing, validation, monitoring, and documentation to ensure analytical data assets are accurate, reliable, transparent, and trusted.

Required Education And Experience

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, Economics, or a related quantitative field, or commensurate work experience required.
• 5 years of experience in analytics engineering, data engineering, business intelligence, or advanced analytics required.Advanced SQL proficiency and experience with Python, dbt, and Git required.
• Experience developing, validating, reconciling, and troubleshooting complex analytical datasets across multiple systems.
• Experience designing analytics-ready datasets required.
• Snowflake experience preferred.
• Experience supporting reporting solutions in Power BI, Sigma, or a comparable platform preferred.
• Banking, consumer lending, financial services, fintech, credit risk, or collections experience preferred.
• Strong communication, analytical, problem-solving, and organizational skills required.

Physical Demands

While performing the duties of this job, the employee is frequently required to sit, stand, walk, visualize, talk, hear, and handle or touch objects or controls. The employee may occasionally lift, push, or pull up to 20 pounds. This position is an office-based position where you must be able to sit for long periods of time. The employee will be working on a computer 90% of the time.