Lead Engineer, Pricing & Decision Systems

One Park Financial•Plano, TX

10d

About The Position

One Park Financial (OPF) is seeking a Lead Engineer to own and evolve its Python pricing engine. This system is critical for determining the terms offered to small businesses in real-time, impacting profitable growth. The role involves being the primary developer and technical steward, building new features, ensuring reliability, and overseeing changes. You will lead and mentor a team of engineers, setting technical direction and review standards. This is a hands-on role where you will still be actively coding, breaking down work, reviewing complex changes, and ensuring the team ships accurately and on time. Collaboration with Product, DevOps, Data Engineering, Data Science, and QA is essential for managing the lifecycle of pricing changes. Initially focused on the pricing engine, the role will expand to integrating machine-learning models into production, building internal analytics applications and data products, and managing the data infrastructure and quality for these applications. The company is AI-forward and expects engineers to leverage modern AI and LLM tooling for efficiency. The goal is to build a broader analytics platform, not just maintain existing systems.

Requirements

5+ years of professional Python development experience, 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 (FastAPI, Flask, or similar).
Hands-on AWS experience operating production services, particularly ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
Proficiency in data correctness in production storage: designing DynamoDB read/write patterns, handling schema changes, and managing versioned data in S3 (Parquet, partitioned datasets).
Strong SQL skills and comfort with relational and NoSQL data and reference files (CSV, Parquet).
Comfort with business-logic-heavy applications where correctness is paramount, including implementing rules from scorecards, reference files, and configuration data.
Familiarity with data modeling and validation frameworks like Pydantic, and a proactive approach to 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 model deployment and iteration.
Experience designing or supporting A/B tests and experimentation, including an understanding of stratification and clean experiment data.
Strong communication and cross-team collaboration skills, able to work with Product, DevOps, Data Engineering, Data Science, and QA.
Self-directed and comfortable 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.
CI/CD and containerized deployments (Docker, ECS).
Analytics-engineering tooling such as dbt or Airflow.
Front-end familiarity (Next.js, TypeScript) for admin and monitoring dashboards.
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).

Responsibilities

Serve as the primary technical owner of the Python-based pricing and decision engine on AWS (FastAPI, ECS/Fargate, DynamoDB, Lambda, SQS, Secrets Manager, CloudWatch).
Implement and integrate new scoring models into the pricing flow, maintaining offer grids, modifiers, and rules for terms, rates, origination fees, and offer sizing.
Own the end-to-end pricing logic, from scoring to offer construction, ensuring business logic correctness before deployment.
Lead and mentor engineers supporting the pricing system, setting architecture, coding, and review standards, and performing code reviews for critical pricing changes.
Own the correctness of data stored in DynamoDB and S3, including audit records, experiment history, and versioned reference files.
Build reconciliation and quality checks to identify problems before they reach production.
Design, run, and analyze A/B tests on pricing strategies, including variant assignment, balancing, and experiment data integrity.
Own the integration with the sales platform, including data flow to and from CRM and external credit/bank-data providers.
Ensure the engine's reliability and observability, maintaining its health dashboard.
Partner with Product on the pricing roadmap and UI, DevOps on deployment and incident response, Data Engineering on upstream data, Data Science on model deployment, and QA on test strategy.
Communicate system behavior and changes clearly to non-technical stakeholders and ensure alignment on shared services, API contracts, and deployment workflows.
Utilize modern AI and LLM tooling to enhance personal productivity in areas like prototyping, documentation, and testing.
Integrate machine-learning models into production, writing feature and scoring logic, and collaborating with Data Science on model iteration.
Build internal analytics applications and data products for other teams.
Own the data storage, plumbing, and quality checks for analytics applications and models.
Set engineering standards and technical direction for analytics development as the team grows.