Key skills
PythonSQLAIMLELTData EngineeringAnalyticsSnowflakeKafkaAzureAzure DevOpsSQL ServerGitGitHubVersion ControlCI/CDCommunicationCollaborationSales
About this role
DriveTime is the largest privately owned used car sales finance & servicing company in the nation, headquartered in Tempe, Arizona. They are seeking a Data Engineer to support the development, modeling, optimization, and governance of data processes, enabling business teams and analytics with high-quality solutions.
Responsibilities:
- Design and maintain data structures that power application and operational workflows reliably and at scale
- Build and operate pipelines that move data between internal systems and key vendor/partner integrations using Snowflake, SQL Server, Argo (workflow orchestrator), and Python
- Develop consumer-ready datasets in Snowflake using ELT best practices, dimensional modeling, and well-documented transformation logic
- Monitor and tune Snowflake performance, pipeline reliability, and cost efficiency across the full data stack
Requirements:
- 4+ years of experience in data engineering, analytics engineering, or a related data discipline
- Bachelor's degree in Information Technology, Computer Science, or related field — or equivalent work experience
- Advanced SQL skills spanning transactional query patterns and analytical/reporting workloads across both relational and cloud data platforms
- Experience with relational database systems (SQL Server) and modern cloud data platforms (Snowflake)
- Familiarity with streaming and event-driven data pipelines (Kafka, CDC, or similar)
- Strong collaboration and communication skills, with the ability to work across technical and non-technical teams
- Solid understanding of data modeling across paradigms — dimensional/star schema for analytics and normalized/3NF design for transactional systems
- Proficiency in Python for scripting, pipeline automation, data validation, and integration work
- Experience with Git-based version control and CI/CD workflows (GitHub or Azure DevOps)
- Openness and an appetite to proactively identify opportunities to leverage AI/ML for automation, anomaly detection, predictive insights, and intelligent decision-making across data pipelines and reporting solutions