Lead Engineer, Pricing & Decision Systems

One Park Financial•Plano, TX

About The Position

One Park Financial is seeking a hands-on Lead Engineer to own and evolve its Python-based pricing and decision engine, which determines terms for new business and renewal offers to small businesses. The role is the system’s primary developer and technical steward, responsible for building capabilities, maintaining reliability, and ensuring pricing changes are correct before release. The lead will set technical direction and review standards for supporting engineers while remaining actively involved in development. This person will coordinate with Product, DevOps, Data Engineering, Data Science, and QA to move pricing changes from specification into production. The position is the first dedicated engineering hire on the analytics team. After establishing a reliable pricing engine, the role will expand to production integration of machine-learning models, internal analytics applications and data products, and the data storage, pipelines, and quality checks on which those products depend. The company expects engineers to use modern AI and LLM tooling to work more efficiently.

Requirements

At least 5 years of professional Python development, including building and maintaining production applications and services.
Experience leading or mentoring engineers, setting standards, reviewing code, and directing a small team's work.
Experience building and maintaining REST APIs using FastAPI, Flask, or similar.
Hands-on experience operating production services on AWS, particularly ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
Experience ensuring production data correctness, including designing DynamoDB access patterns, handling schema changes and type boundaries, and managing versioned S3 data such as Parquet and partitioned datasets.
Strong SQL skills and comfort with relational and NoSQL data and reference files such as CSV and Parquet.
Comfort working with business-logic-heavy applications where correctness matters, including implementing rules from scorecards, reference files, and configuration data.
Familiarity with data modeling and validation frameworks such as Pydantic, and a practice of building reconciliation and QA checks.
Hands-on experience integrating machine-learning or analytical model outputs into production systems, including feature engineering and scoring logic, and collaborating with Data Science on deployment and iteration.
Experience designing or supporting A/B tests and experimentation, including stratification and maintaining clean experiment data.
Strong communication and cross-team collaboration skills across Product, DevOps, Data Engineering, Data Science, and QA.
Self-directed approach and comfort owning a system end to end.
Comfort using modern AI and LLM tools to improve work efficiency.

Nice To Haves

Background in financial services, lending, or pricing systems.
Experience with CRM or platform APIs such as Salesforce or HubSpot.
Experience with CI/CD and containerized deployments, including Docker and ECS.
Experience with analytics-engineering tools such as dbt or Airflow.
Front-end familiarity, including Next.js or TypeScript, for admin and monitoring dashboards.
Experience building AI infrastructure and agents, such as RAG, LangChain, LangGraph, or Agents SDK.
Exposure to ML model serving and deployment tools, such as model monitoring, retraining pipelines, MLflow, or SageMaker.

Responsibilities

Own and evolve the Python-based pricing and decision engine deployed on AWS, including FastAPI, ECS/Fargate, DynamoDB, Lambda, SQS, Secrets Manager, and CloudWatch.
Implement and integrate scoring models into the pricing flow based on Data Science specifications; maintain offer grids, modifiers, and rules for terms, rates, origination fees, and offer sizing.
Own pricing logic from scoring through offer construction and verify business logic before release.
Lead and mentor engineers supporting the system; set architecture, coding, and review standards; review pricing-related changes and clearly specify risky work.
Ensure correctness of engine data stored in DynamoDB and S3, including decision audit records, experiment history, and versioned pricing reference files.
Build reconciliation and quality checks to identify data problems before they reach production.
Design, run, and analyze A/B tests for pricing strategies, including eligibility, variant assignment and balancing, and experiment-data integrity.
Own integration with the sales platform and data flows to and from the CRM and external credit and bank-data providers.
Maintain engine reliability and observability, including the production health dashboard.
Partner with Product on the pricing roadmap and pricing UI; work with DevOps on deployment and incident response, Data Engineering on upstream data and pipelines, Data Science on model deployment, and QA on test strategy and coverage.
Communicate system behavior and changes to non-technical stakeholders and align teams on shared services, API contracts, and deployment workflows.
Use modern AI and LLM tooling for prototyping, documentation, testing, and other work.
As the analytics platform expands, integrate machine-learning models into production, implement feature and scoring logic, and partner with Data Science on model iteration.
Build internal analytics applications and data products, including automated reporting and decision-support tools.
Own data storage, plumbing, and quality checks for analytics applications and models.
Set engineering standards and technical direction for analytics development as the team grows.