Data Engineer, Production Analytics

Carvana•Tempe, AZ

About The Position

Have you ever wished the analytics projects you worked on could have visible and tangible benefits? The production analytics team owns advanced analytics (data science, optimization, data mining, etc.) in Carvana’s vertically integrated vehicle inspection and reconditioning centers. Our team can walk through these centers and see our work in action. We're launching a high-priority initiative to build a clean analytical data layer that will make running future analysis faster than ever. By creating curated fact tables with the right level of granularity, we'll enable our team to focus on generating insights and driving business impact. As a Data Engineer, you'll work closely with our Lead Data Engineer to design, implement, and own these foundational systems. This is a build-and-own role with high visibility across the organization due to its strategic importance.

Requirements

Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
Strong SQL skills - you can write complex queries involving joins, aggregations, window functions, and CTEs.
Proficiency in Python for data processing and automation.
Understanding of data warehouse concepts and dimensional modeling fundamentals.
Familiarity with Git-based version control, branching, and pull requests.
Strong analytical mindset with attention to detail - you don't assume data is correct, you validate it.
Excellent communication skills to explain technical concepts clearly.
Eagerness to learn and grow through technical mentorship and code reviews.
A relentless drive to push work into production and see tangible impact.
An attitude that rocks.

Nice To Haves

Experience with Snowflake or other cloud-based data warehouses (Google Cloud Platform, AWS, Azure).
Hands-on experience with BI tools like Tableau and/or Sigma, understanding how analysts consume data.
Knowledge of data quality validation techniques and testing frameworks.
Experience building APIs (e.g., FastAPI, Flask) or consuming APIs as data sources.
Exposure to manufacturing or operations analytics environments.
Understanding of Agile/iterative development practices.
Experience with containerized code (Docker, Kubernetes).
Familiarity with DevOps principles and CI/CD pipelines.

Responsibilities

Building and maintaining curated analytical data layer: Reverse engineering existing BI reporting systems to understand embedded business logic and calculation rules.
Designing and implementing clean fact tables in Snowflake that pre-apply business rules and handle data quality issues.
Developing robust stored procedures using zero-downtime deployment patterns.
Creating validation frameworks to ensure data accuracy against production systems.
Converting complex business logic into maintainable data structures.
Owning and maintaining the data systems you build, iterating as business needs evolve.
Supporting broader data engineering initiatives: Building and maintaining ETL pipelines for analytics workflows.
Supporting, and perhaps developing, APIs to support data science projects and operational systems.
Collecting data from external APIs and web sources.
Building prototype dashboards in Sigma to support our BI platform transition.
Contributing to the team's Python codebase for data processing and automation.
Collaborating and growing: Working under the technical direction of our Lead Data Engineer with daily code reviews and architectural guidance.
Documenting data models, business rules, and technical decisions.
Participating in architecture discussions and design reviews.
Partnering with analysts to understand analytical requirements and pain points.