Lead Engineer, Pricing & Decision Systems

One Park Financial•Miami, FL

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 in real time. The role is the primary technical ownership position for the engine, responsible for building capabilities, maintaining reliability, and ensuring changes are correct before release. The lead will guide and mentor supporting engineers, establish technical direction and review standards, and remain actively involved in coding. The role works across Product, DevOps, Data Engineering, Data Science, and QA to deliver pricing changes from specification through production. The pricing engine runs on AWS and handles scoring, offer construction, pricing rules, and supporting data. The position also owns experiment design and integrity, integrations with the sales platform, CRM, and external credit and bank-data providers, and the engine’s production monitoring and data quality. As the first dedicated engineering hire on the analytics team, the role is expected to expand beyond pricing into a broader analytics platform. This includes integrating machine-learning models into production, building internal analytics applications and data products, owning their data plumbing and quality checks, and establishing engineering standards as the analytics team grows. The company expects engineers to use modern AI and LLM tools to work more efficiently. One Park Financial is a financial technology company that connects small businesses with financing and funding options. It describes its culture as collaborative, innovative, inclusive, and supportive of employee growth.

Requirements

At least 5 years of professional Python development, including 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 designing DynamoDB read and write access patterns, handling schema changes and type boundaries, and managing versioned S3 data such as Parquet and partitioned datasets.
Strong SQL skills 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 implementing rules from scorecards, reference files, and configuration data.
Familiarity with data modeling and validation frameworks such as Pydantic, and an inclination to build 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, including working across 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.
Experience with CI/CD and containerized deployments such as Docker and ECS.
Experience with analytics-engineering tools such as dbt or Airflow.
Front-end familiarity with Next.js or TypeScript for admin and monitoring dashboards.
Experience building AI infrastructure and agents, including RAG, LangChain, LangGraph, or Agents SDK.
Exposure to machine-learning model serving and deployment tooling, including model monitoring, retraining pipelines, MLflow, or SageMaker.

Responsibilities

Own the Python-based pricing and decision engine deployed on AWS, and develop new capabilities while maintaining its reliability.
Implement and integrate scoring models into pricing flows based on Data Science specifications; maintain offer grids, modifiers, and rules governing 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; establish architecture, coding, and review standards, conduct reviews of pricing-related changes, and clearly specify risky work.
Ensure the correctness of engine data stored in DynamoDB and S3, including per-decision audit records, experiment history, and versioned pricing reference files.
Build reconciliation and data-quality checks that identify problems before they reach production.
Design, run, and analyze A/B tests, including eligibility, variant assignment and balance, 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; coordinate 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 tools for tasks such as prototyping, documentation, and testing.
As the analytics platform expands, integrate production machine-learning models by implementing feature and scoring logic and partnering with Data Science on iteration.
Build internal analytics applications and data products, including automated reporting and decision-support tools.
Own data storage, plumbing, and quality checks used by analytics applications and models.
Set engineering standards and technical direction for analytics development as the team grows.