Lead Pricing & Decision Systems Engineer — Real-Time FinTech

One Park Financial; Miami (FL); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

401k with match

ID protection

Life Insurance

Generous PTO

Job summary

One Park Financial in the United States seeks a Lead Engineer to own the Python-based pricing engine that prices every new business and renewal offer. You will lead the technical direction, review standards, and coordinate with Product, DevOps, Data Engineering, Data Science, and QA to ship pricing changes with confidence.

This hands-on role evolves toward integrating machine-learning models into production, building analytics tools, and owning data pipelines.

Qualifications

• 5+ years of professional Python development, building production applications and services.
• Experience leading or mentoring engineers and setting standards.
• Experience building and maintaining REST APIs (FastAPI, Flask, or similar).
• Hands-on AWS experience with ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, CloudWatch.

Responsibilities

• Serve as the primary technical owner of the Python-based pricing engine on AWS.
• Lead engineers, set architecture and review standards, and own risky changes.
• Own end-to-end pricing logic and data correctness in production storage (DynamoDB, S3).
• Design, run, and analyze A/B tests on pricing strategies and analytics platform growth.

Skills

Python

Leadership

REST APIs

AWS

Data modeling

SQL

Pydantic

A/B testing

AI/LLM tooling

Tools

ECS/Fargate

DynamoDB

S3

Lambda

SQS

Secrets Manager

CloudWatch

FastAPI

Flask

PostgreSQL

Parquet

Job description

One Park Financial in the United States seeks a Lead Engineer to own the Python-based pricing engine that prices every new business and renewal offer. You will lead the technical direction, review standards, and coordinate with Product, DevOps, Data Engineering, Data Science, and QA to ship pricing changes with confidence.

This hands-on role evolves toward integrating machine-learning models into production, building analytics tools, and owning data pipelines.