Lead Engineer, Pricing & Decision Systems

One Park Financial•Plano, TX

11d

About The Position

One Park Financial (OPF) is seeking a Lead Engineer to own and evolve the Python pricing engine that determines terms for new and renewal business offers in real-time. This role is a hands-on, technical leadership position responsible for building new capabilities, ensuring system reliability, and overseeing the accuracy of all changes. The Lead Engineer will also mentor supporting engineers, set technical direction, and coordinate with various teams including Product, DevOps, Data Engineering, Data Science, and QA. Initially focused on the pricing engine, the role will expand to integrate machine-learning models into production, build internal analytics applications and data products, and manage the associated data infrastructure. The company is AI-forward, expecting engineers to leverage modern AI and LLM tooling for efficiency.

Requirements

5+ years of professional Python development, including building and maintaining production applications and services, not just scripts or notebooks.
Experience leading or mentoring engineers: setting standards, reviewing others' code, and directing a small team's work.
Experience building and maintaining REST APIs (FastAPI, Flask, or similar).
Hands-on AWS experience operating production services, particularly ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
Real care for data correctness in production storage: designing DynamoDB read and write access patterns, handling schema changes and type boundaries cleanly, and managing versioned data in S3 such as Parquet and partitioned datasets. You should be able to talk about a time a data-storage bug bit you and how you would prevent it.
Strong SQL, and comfort working with relational and NoSQL data and reference files such as CSV and Parquet.
Comfort with business-logic-heavy applications where correctness matters, including reading and correctly implementing rules from scorecards, reference files, and configuration data.
Familiarity with data modeling and validation frameworks such as Pydantic, and the instinct to build reconciliation and QA checks rather than trusting writes blindly.
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 model deployment and iteration.
Experience designing or supporting A/B tests and experimentation, including an appreciation for stratification and clean experiment data.
Strong communication and cross-team collaboration, able to work across Product, DevOps, Data Engineering, Data Science, and QA, each with a different technical context, and keep them aligned.
Self-directed, and comfortable owning a system end to end.
Comfort using modern AI and LLM tools to make your own work more efficient.

Nice To Haves

Background in financial services, lending, or pricing systems.
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 AI infrastructure and agents (RAG, LangChain, LangGraph, Agents SDK).
Exposure to ML model serving and deployment tooling (model monitoring, retraining pipelines, MLflow or SageMaker). Not required for day one, but an area this role grows into as we build out the analytics platform

Responsibilities

Serve as the primary technical owner of our Python-based pricing and decision engine, deployed on AWS (FastAPI, ECS/Fargate, DynamoDB, Lambda, SQS, Secrets Manager, CloudWatch).
Implement and integrate new scoring models into the pricing flow, working from Data Science specifications, and maintain the offer grids, modifiers, and rules that set terms, rates, origination fees, and offer sizing.
Own the pricing logic end to end, from scoring through offer construction, and ensure the business logic is correct before it ships. Treat a mispriced offer as the serious incident it is.
Lead and mentor the engineers supporting the system. Set architecture, coding, and review standards, own code review for anything that touches pricing, and spec the risky changes clearly so the team ships accurately.
Own the correctness of the data the engine stores in DynamoDB and S3, including per-decision audit records, experiment history, and the versioned reference files that drive pricing. Build the reconciliation and quality checks that catch problems before they reach production, not weeks later in an analysis.
Design, run, and analyze A/B tests on pricing strategies, including who is eligible, how variants are assigned and balanced, and the integrity of the experiment data you later measure against.
Own the integration with the sales platform the engine prices for, and the data flow to and from the CRM and external credit and bank-data providers.
Keep the engine reliable and observable, and maintain the health dashboard the team uses to monitor it in production.
Partner with Product on the pricing roadmap and the pricing UI, with DevOps on deployment and incident response, with Data Engineering on the upstream data and pipelines the engine depends on, with Data Science on model deployment, and with QA on test strategy and coverage.
Communicate system behavior and changes clearly to non-technical stakeholders, and keep these teams aligned on shared services, API contracts, and deployment workflows.
Use modern AI and LLM tooling to speed up your own work, from prototyping to documentation to testing.
Integrate machine-learning models built by our data science and marketing analytics teams into production, writing the feature and scoring logic that puts a model's output to work in the pricing flow and other decisions, and partnering with Data Science on iteration.
Build the internal analytics applications and data products other teams depend on, from automated reporting to decision-support tools.
Own the data storage, plumbing, and quality checks those analytics applications and models depend on.
Set the engineering standards and technical direction for analytics development as the team grows around you.