Lead Engineer, Pricing & Decision Systems

About the role

Workplace

• The role is identified as on-site in Plano, Texas.

Core responsibilities

• Own the AWS-hosted Python pricing and decision engine.
• Implement scoring models and maintain offer grids, modifiers, rates, fees, and offer-sizing rules.
• Ensure pricing business logic is correct before deployment.
• Lead and mentor engineers and set architecture, coding, and review standards.
• Own DynamoDB and S3 data correctness, audit records, experiment history, and versioned reference files.
• Build reconciliation and quality checks for production data and pricing.
• Design, run, and analyze A/B tests for pricing strategies.
• Own sales-platform, CRM, credit-provider, and bank-data integrations.
• Maintain reliability, observability, and the production health dashboard.
• Coordinate with Product, DevOps, Data Engineering, Data Science, and QA.
• Communicate system behavior, API contracts, and deployment changes clearly.

Platform growth

• Integrate machine-learning models into production pricing and other decision systems.
• Build internal analytics applications and data products, including reporting and decision-support tools.
• Own the data storage, plumbing, and quality checks supporting analytics applications and models.
• Set engineering standards and technical direction for analytics development as the team grows.
• Use modern AI and LLM tooling for prototyping, documentation, testing, and delivery.

Requirements

• 5+ years of professional Python development for production applications and services.
• Experience leading or mentoring engineers and directing a small team's work.
• Experience building and maintaining REST APIs with FastAPI, Flask, or similar.
• Hands-on production AWS experience, including ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
• Experience with DynamoDB access patterns, schema and type boundaries, and versioned S3 data such as Parquet.
• Strong SQL and comfort with relational, NoSQL, CSV, and Parquet data.
• Ability to implement business logic from scorecards, reference files, and configuration data.
• Familiarity with Pydantic or similar data modeling and validation frameworks.
• Experience integrating machine-learning or analytical model outputs into production.
• Experience with A/B testing, experimentation, stratification, and clean experiment data.
• Strong communication and cross-team collaboration.
• Self-directed end-to-end ownership and comfort with modern AI and LLM tools.

Nice to have

• Background in financial services, lending, or pricing systems is advantageous.
• Experience with Salesforce, HubSpot, or similar CRM and platform APIs is advantageous.
• CI/CD and containerized deployment experience is advantageous.
• Experience with dbt, Airflow, Next.js, or TypeScript is advantageous.
• Experience with AI infrastructure, agents, RAG, LangChain, LangGraph, or Agents SDK is advantageous.
• Exposure to model monitoring, retraining pipelines, MLflow, or SageMaker is advantageous.

Benefits

• Benefits include health, dental, and vision insurance, a medical bridge plan, and 401(k) matching.
• The company provides identity protection and life insurance coverage, plus paid time off and holidays.