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# Lead Data Engineer

- **Company:** PEPR LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Data Analysis, Automation of Tests, Github, Data Processing, Data Strategy, Git
- **Published:** September 19, 2026
- **Apply:** https://computerjobs.com/us/en/mob/job/60FAD0F2D4D0EBE8F9

## About the Role

Advanced proficiency in R, including:
Data manipulation (eg, dplyr, data.table)
Statistical modelling and simulation
Building reproducible analytical pipelines
Automated testing and documentation
Version control using Git/GitHub, The successful candidates will be a strong collaborators, able to work effectively with the customer's teams, digital partners, regulators and external stakeholders. You will communicate complex analysis clearly to non-technical audiences and represent the customer confidently in industry-facing settings. Strong leadership skills are essential whether managing a small team or coordinating delivery across teams alongside the ability to build analytical capability and contribute to wider improvement work.

## Description

Develop, maintain, quality assure and operate the numerical models used to calculate pEPR LA payments & producer fees, ensuring they are robust, transparent, fit for purpose.
Lead the data strategy for the Fees and Payments Calculator teams, identifying data needs, developing processes for gathering and maintaining essential datasets, and working with wider data strategy teams to ensure alignment and reduce duplication
Key Responsibilities
Work collaboratively across the customer, ensuring data and evidence are used effectively to support decision making, policy development, and external communications. Develop, maintain, quality assure and operate the numerical models used to calculate pEPR fees twice a year, ensuring they remain robust, transparent, and fit for purpose.
Oversee access to and use of the Report Packaging Data (RPD) portal, coordinating with digital teams and regulators to resolve technical issues, and further develop R-based tools for extracting and analysing producer data.


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