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-timeSalt Lake City • Utah • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

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, establish technical standards, and communicate effectively with cross-functional stakeholders.

Highest-signal resume keywords

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

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Python DevelopmentSQLDynamoDBData ModelingMachine Learning IntegrationA/B TestingREST API DevelopmentData ValidationBusiness Logic ImplementationAnalytics Development

Soft Skills

LeadershipMentoringCommunicationCollaborationSelf-Directed

Tools & Technologies

AWS (Lambda, SQS, CloudWatch)FastAPIFlaskPydanticDockerDbtAirflowMLflowSageMakerNext.js

Industry Keywords

Financial ServicesLendingPricing SystemsCRM IntegrationAnalytics 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 engineers supporting the system; 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 data problems before production impact.
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 reliability, observability, and the production health dashboard.
Partner with Product, DevOps, Data Engineering, Data Science, and QA on roadmap, deployment, incidents, model deployment, and test coverage.
Communicate system behavior and changes to non-technical stakeholders.
Use 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.
Establish 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 CSV and Parquet reference files.
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.
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: CRM or platform API experience 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 with Next.js and TypeScript.
Nice to have: AI infrastructure and agent experience (RAG, LangChain, LangGraph, Agents SDK).
Nice to have: ML model serving and deployment tooling such as 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
Innovative technology environment
Supportive and inclusive work environment