Senior Data Engineer (DBT)

DriveTime Family of Brands is the largest privately owned used car sales finance & servicing company in the nation. The Senior Data Engineer role is responsible for designing, building, and delivering scalable data models that power analytics and AI initiatives, while acting as a mentor and leader within the data services organization.

Responsibilities:

• Owning the design and development of robust dbt Core models that transform raw data into trusted, analytics‑ready datasets in Snowflake
• Architecting scalable, high‑performance data models that support enterprise reporting, analytics, and AI use cases
• Translating complex business and analytical requirements into efficient, well‑structured ELT solutions through close collaboration with BI, analytics, and business stakeholders
• Embedding best practices in data quality, testing, documentation, and lineage to ensure transparency, reliability, and trust in our data ecosystem
• Leveraging Python to support automation, data validation, orchestration, and performance monitoring across ELT pipelines
• Monitoring, tuning, and optimizing Snowflake query performance and cost efficiency
• Leading technical design discussions and contributing hands‑on to critical data initiatives
• Serving as a technical lead and mentor, guiding other engineers and elevating standards across the full data transformation lifecycle
• Providing thought leadership on modern data transformation patterns, tooling, and architecture to help shape enterprise data strategy
• Supporting data governance and metadata enrichment initiatives in alignment with broader enterprise data goals

Requirements:

• 5+ years of experience in data engineering or analytics engineering
• Bachelor's degree in Information Technology or a related field, or equivalent practical experience
• Advanced SQL skills with deep, hands-on experience using dbt Core for data transformation, testing, and documentation
• Experience with dbt Core
• Strong expertise with Snowflake or a similar modern cloud data platform
• Proficiency in Python for scripting, automation, and performance tuning
• Solid understanding of dimensional modeling, ELT principles, and data warehousing best practices
• Experience with Git-based version control and CI/CD workflows (e.g., GitHub, Azure DevOps, Argo)
• Demonstrated ability to lead technical initiatives and mentor other engineers
• Strong collaboration skills and a proven ability to influence and drive adoption of modern data engineering best practices
• Experience supporting enterprise data governance or metadata management initiatives
• Prior involvement in large-scale analytics or AI-driven data platforms