Overview

Software Engineer, Analytics Pricing & Decision Systems

Responsibilities

• Innovative Environment: Work with cutting-edge technology and be part of a team that is constantly pushing the boundaries of fintech.
• Professional Growth: We invest in our employees' growth with continuous learning opportunities, training programs, and career advancement paths.
• Supportive Culture: Enjoy a supportive and inclusive work environment where your ideas are valued, and your contributions make a real impact.
• Community Focus: Be part of a company that understands the importance of small and mid-sized businesses to their communities and the nation's financial health.
• High-Performing Team: Join a team of badasses who are committed to excellence and are integral to our company's expansion and success.
• Build and maintain features in our Python-based pricing and decision engine, deployed on AWS (FastAPI, ECS/Fargate, DynamoDB, S3, Lambda, SQS, CloudWatch), working under the direction and code review of the Lead Engineer.
• Implement new pricing rules and scoring updates from clear specifications maintaining the offer grids, modifiers, and rules that set terms, rates, origination fees, and offer sizing and verify each change produces the right offer before it ships.
• Treat a mispriced offer as the serious incident it is: test your changes thoroughly, reason about how an edit flows through the offer math, and never ship a pricing change you have not proven correct.
• Help keep the data the engine writes to DynamoDB and S3 correct and complete per-decision audit records, experiment history, and the versioned reference files that drive pricing and build the validation and reconciliation checks that catch problems early.
• Support the integrations between the engine and the systems around it: the sales platform it prices for, the CRM, and the external credit and bank-data providers.
• Help keep the engine reliable and observable, and support the health dashboard the team uses to monitor it in production.
• Write clean, tested, reviewable code, and grow into deeper ownership of parts of the system over time.
• Collaborate with Data Science, QA, DevOps, and the sales-platform team as you take changes from specification to production.
• Use modern AI and LLM tooling to speed up your own work, from prototyping to documentation to testing.

Qualifications

• 24 years of professional Python development, including building or maintaining real applications and services, not just scripts or notebooks.
• Solid fundamentals in writing clean, tested, readable code, and a willingness to have your work reviewed and to learn from that review.
• Some experience with REST APIs (FastAPI, Flask, or similar), or a clear ability to pick them up quickly.
• Working familiarity with AWS, or strong fundamentals and genuine eagerness to learn our stack (ECS/Fargate, DynamoDB, S3, Lambda, SQS, CloudWatch). Deep production AWS operations experience is not expected on day one.
• Comfort with SQL and working with structured data files such as CSV and Parquet.
• Care for correctness in business-logic-heavy code: the instinct to read a rule carefully, implement it exactly, and check the result rather than assume it worked. This matters to us more than years on a rsum.
• Exposure to data validation or modeling frameworks such as Pydantic is a plus, along with an interest in building QA and reconciliation checks rather than trusting writes blindly.
• Strong communication able to ask good questions, follow a specification, and flag when something looks wrong.
• Eager to learn, coachable, and comfortable owning tasks end to end with guidance from a senior engineer.
• Comfort using modern AI and LLM tools to make your own work more efficient.

Nice to have

• Background or coursework in financial services, lending, or pricing systems.
• Exposure to DynamoDB or other NoSQL stores, and to versioned data in S3 (Parquet, partitioning).
• Any experience integrating machine learning or analytical model outputs into an application feature engineering, scoring logic, or model deployment.
• Exposure to A/B testing or experimentation and the idea of keeping experiment data clean.
• Experience with CRM or platform APIs such as Salesforce or HubSpot.
• CI/CD and containerized deployments (Docker, ECS).
• Analytics-engineering tooling such as dbt or Airflow.
• Front-end familiarity (Next.js, TypeScript) for the admin and monitoring dashboard.
• Experience building with AI infrastructure and agents (RAG, LangChain, LangGraph, Agents SDK).