Lead Engineer, Pricing & Decision Systems

One Park Financial•Salt Lake City, UT

About The Position

One Park Financial is seeking a hands-on Lead Engineer to own and evolve its Python pricing and decision engine, which determines terms for new-business and renewal offers to small businesses. The engineer will be the system’s primary developer and technical steward, responsible for building capabilities, maintaining reliability, and ensuring pricing changes are correct before release. The role leads and mentors supporting engineers while remaining actively involved in coding, work planning, reviews, and delivery. The position coordinates with Product, DevOps, Data Engineering, Data Science, and QA to take pricing changes from specification through production. The engine runs on AWS and supports pricing decisions, including scoring, offer construction, rates, fees, and offer sizing. The role also owns the correctness of stored decision data, audit records, experiment history, reference files, integrations, monitoring, and quality checks. This is the first dedicated engineering hire on the analytics team. After establishing the pricing engine’s reliability, the role is expected to expand into integrating machine-learning models into production, building internal analytics applications and data products, owning their data plumbing and quality checks, and setting technical direction as the analytics team grows. The company expects engineers to use modern AI and LLM tools to improve the speed and quality of their work.

Requirements

At least 5 years of professional Python development, including building and maintaining 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 AWS experience operating production services, particularly ECS/Fargate, DynamoDB, S3, Lambda, SQS, Secrets Manager, and CloudWatch.
Experience ensuring correctness of production data storage, including DynamoDB read/write access patterns, schema changes, type boundaries, and versioned S3 data such as Parquet and partitioned datasets.
Strong SQL skills and comfort working with relational and NoSQL data as well as 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 the ability 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, with the ability to align Product, DevOps, Data Engineering, Data Science, and QA.
Self-directed approach and comfort 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.
Familiarity with analytics-engineering tools such as dbt or Airflow.
Front-end familiarity, including Next.js or TypeScript, for admin and monitoring dashboards.
Experience building AI infrastructure and agents, including RAG, LangChain, LangGraph, or Agents SDK.
Exposure to ML model serving and deployment tooling, such as model monitoring, retraining pipelines, MLflow, or SageMaker.

Responsibilities

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 based on Data Science specifications.
Maintain offer grids, modifiers, and rules that determine 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; set architecture, coding, and review standards; review changes affecting pricing; and clearly specify risky changes.
Own 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 quality checks to detect data or pricing issues before they reach production.
Design, run, and analyze A/B tests, including eligibility, variant assignment and balancing, and experiment-data integrity.
Own the 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; DevOps on deployments 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 to support prototyping, documentation, testing, and other work.
Integrate machine-learning models built by Data Science and marketing analytics teams into production, including feature and scoring logic, and partner on model iteration.
Build internal analytics applications and data products, including automated reporting and decision-support tools.
Own data storage, plumbing, and quality checks supporting analytics applications and models.
Set engineering standards and technical direction for analytics development as the team grows.