Lead Engineer, Pricing & Decision Systems

One Park Financial

. Serve as the primary technical owner of the Python-based pricing and decision engine deployed on AWS.

Posted 10/24/2026full-timeMiami • Florida • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in Python development and AWS services, with a strong focus on building and maintaining production applications, integrating machine learning models, and ensuring data integrity. Proven ability to lead engineering teams, set technical standards, and collaborate effectively across multiple domains.

Highest-signal resume keywords

Python DevelopmentAWS Services (ECS/Fargate, DynamoDB, S3)Machine Learning IntegrationREST API Development (FastAPI, Flask)Data Modeling and Validation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLDynamoDBS3REST APIsMachine LearningA/B TestingData ModelingAnalytics EngineeringCI/CD

Soft Skills

LeadershipCommunicationCollaborationSelf-DirectedMentoring

Tools & Technologies

AWSDockerDbtAirflowCloudWatchFastAPIFlaskPydanticMLflowSageMaker

Industry Keywords

Financial ServicesLendingPricing SystemsCRMData Science

Tech Stack

Tools & technologies

AirflowAWSDockerDynamoDBFlaskJavaScriptNext.jsNoSQLPythonSQLTypeScript

About the role

Key responsibilities & impact

Serve as the primary technical owner of the Python-based pricing and decision engine deployed on AWS.
Implement and integrate scoring models into the pricing flow and maintain offer grids, modifiers, and rules governing terms, rates, origination fees, and offer sizing.
Own pricing logic end to end and ensure business logic is correct before release.
Lead and mentor supporting engineers; set architecture, coding, and review standards.
Own correctness of data stored in DynamoDB and S3, including audit records, experiment history, and versioned reference files.
Build reconciliation and quality checks to detect production data problems early.
Design, run, and analyze A/B tests on pricing strategies.
Own integration with the sales platform, CRM, and external credit and bank-data providers.
Maintain engine reliability, observability, and the production health dashboard.
Partner with Product, DevOps, Data Engineering, Data Science, and QA on roadmap, deployment, incident response, model deployment, testing, and coverage.
Communicate system behavior and changes to non-technical stakeholders.
Use modern AI and LLM tooling for prototyping, documentation, and testing.
Integrate machine-learning models into production and build feature and scoring logic.
Build internal analytics applications and data products.
Own data storage, plumbing, and quality checks for analytics applications and models.
Set engineering standards and technical direction for analytics development.

Requirements

What you’ll need

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.
Experience 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.
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.
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.
Self-directed, and comfortable owning a system end to end.
Comfort using modern AI and LLM tools to make your own work more efficient.
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).

Benefits

Comp & perks

Local & National Health Insurance
Dental and Vision insurance
Group Medical Bridge
401k with Match
ID Protection: 100% covered by the company
Life Insurance: 100% covered by the company
Generous PTO and holidays
Continuous learning opportunities
Training programs
Career advancement paths
Supportive and inclusive work environment
Innovative environment with cutting-edge technology