Senior Data Engineer, Predictive Modeling

About Carvana...

At Carvana, we’re changing the way people buy and sell cars. With an ambitious vision and a fundamentally different approach designed to be fun, fast, and fair, Carvana became the fastest-growing automotive retailer in history. We expanded nationally, went public on the New York Stock Exchange, sold our 1 millionth car, and reached the Fortune 500, all in just eight years.

Today, with 4 million retail customers and counting, Carvana is both the fastest-growing and the most profitable public automotive retailer, and we’re just getting started. We continue to raise the bar for our customers as we tackle the enormous opportunity still ahead in the largest consumer vertical.

Working here means being part of a team that embraces change, celebrates creative problem solving, and always strives to be better. At Carvana, you’ll have the opportunity to take on meaningful challenges, learn quickly, and help shape the future of automotive retail. If you’re driven to grow and make an impact as part of a collaborative team, you’ll fit right in. Learn more about what it’s like to work here from the people that already do. 

Work Model: This is a 100% on-site role at our Tempe office, Monday through Friday.

About the team and position

In today’s world, data is king and this is the team with all the data. If you’re excited about understanding complex data sets from disparate sources, this is the team for you. Our data science and analytics team automates everything Carvana does from modeling consumer behavior to understanding how to make our users’ lives better. 

We’re not just building data pipelines; we’re engineering intelligent systems that predict the future and automate complex decisions. This team takes data from across Carvana and external sources to understand past consumer behavior and predict future trends. Whether we’re assessing credit risk, optimizing inventory and pricing strategies, or building AI agents that accelerate our analysts’ productivity, we engineer solutions that directly impact millions of customers. As a Senior Data Engineer on this team, you’ll help architect the technical foundation that makes our data science magic possible and scalable. 

What you’ll be doing

• Refactor and productionalize Python code from Data Scientists and Analysts, transforming experimental notebooks into reliable, maintainable, and production-ready applications with proper testing, error handling, and documentation.
• Design, architect, and maintain robust, scalable predictive modeling data pipelines across our data science ecosystem.
• Design, develop, and maintain internal tooling that accelerates productivity for our Analysts, Data Scientists, and Data Engineers.
• Support data scientists and software engineers in building and deploying new RESTful services.
• Apply software engineering best practices including code reviews, version control, testing frameworks, and continuous integration to ensure high-quality, maintainable codebases.
• Design and develop high-availability applications using technologies like Docker and Kubernetes with focus on scalability, reliability, and observability.
• Develop comprehensive solutions for application logging, error reporting, alerting, and task scheduling across distributed systems.
• Design both relational and non-relational data models for optimal storage and retrieval, considering performance, cost, and maintainability.
• Create robust ETL/ELT processes to integrate data between different systems and formats, ensuring data quality and lineage tracking.
• Design processes that contain sensitive data in a responsible manner (using certificates, hashing, AD permissions), ensuring that necessary security practices are followed.
• Read beyond initial project specifications to identify opportunities for improvement, additional functionality, and technical debt reduction.
• Collaborate with stakeholders to translate business requirements into scalable technical solutions.
• Mentor junior engineers and provide technical guidance on software engineering practices and data architecture decisions.
• Stay current with emerging technologies in data engineering, AI/ML tooling, and agentic workflows that can enhance team capabilities. 
• Excellent communication skills to explain technical concepts clearly
• Other duties as assigned.