Staff Data Engineer, Financial Data Operations

Company: Root Inc.

Location: Location not specified (Remote)

Type: Contract

Remote: Yes

Posted: 2026-05-22

About this role

Root is on a mission to unbreak insurance by creating experiences people love at prices they can’t believe. We believe that investing in world-class technology will facilitate a new class of insurance products, driving a massive positive impact on the hundreds of millions of drivers who carry auto insurance in the US. Root’s Engineering team is committed to building a flexible platform on which our product designers and quantitative scientists can quickly test ideas, deploy them into production, and iterate, with the ultimate objective of a delightful customer experience coupled with effective risk management.


**The Opportunity**
We are looking for a Staff Data Engineer to join our Data Platform team to drive financial data operations at Root, including commissions payments, incentive programs, and other critical backend financial systems. In this role, you will lead complex technical initiatives across engineers within your domain, ensuring the accuracy, scalability, and reliability of data pipelines that power financial processes. You will partner closely with Finance, Product, and Engineering stakeholders to define strategy, shape the technical roadmap, and deliver systems that support high-stakes financial workflows. As a Staff Data Engineer, you will operate as a technical leader, making architectural decisions and delivering solutions with lasting impact over quarters and years.


At Root we’ve built an end-to-end financial data operations solution with multiple data collection tools, scoring pipelines, and analytics interfaces. Our analysts, data scientists and finance teams leverage this system to build payments and incentives programs. We’re looking to drastically increase our capabilities in this space. Your role is to achieve platform level tooling to allow faster onboarding of data sources, integrate data with partners, create faster experimentation cycles, and build the analytics and data science data infrastructure to enable more rapid de...

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