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-timePlano • Texas • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience 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 departments.

Highest-signal resume keywords

Python DevelopmentAWS Services (ECS/Fargate, DynamoDB, S3)REST API Development (FastAPI, Flask)Machine Learning IntegrationA/B Testing Design

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Hard Skills

Python DevelopmentAWS ServicesREST API DevelopmentSQLDynamoDBData ModelingMachine Learning IntegrationA/B TestingAnalytics ApplicationsBusiness Logic Implementation

Soft Skills

LeadershipMentoringCommunicationCollaborationSelf-Directed

Tools & Technologies

DynamoDBS3FastAPIFlaskSQLPydanticAI ToolsCI/CD (Docker, ECS)Analytics-Engineering Tooling (dbt, Airflow)MLflow

Industry Keywords

Financial ServicesLendingPricing SystemsCRM APIsData Engineering

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.
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 issues early.
Design, run, and analyze A/B tests on pricing strategies.
Own integrations 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 roadmaps, deployments, incidents, pipelines, 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.
Build internal analytics applications and data products.
Own analytics data storage, plumbing, and quality checks.
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, 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 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 stratification and clean experiment data.
Strong communication and cross-team collaboration 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.
Nice to have: background in financial services, lending, or pricing systems.
Nice to have: experience with CRM or platform APIs such as Salesforce or HubSpot.
Nice to have: CI/CD and containerized deployments (Docker, ECS).
Nice to have: analytics-engineering tooling such as dbt or Airflow.
Nice to have: front-end familiarity (Next.js, TypeScript).
Nice to have: AI infrastructure and agents (RAG, LangChain, LangGraph, Agents SDK).
Nice to have: ML model serving and deployment tooling, including 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
Cutting-edge technology and innovative work environment