Data Engineer

AutoSavvy•Woods Cross, UT

1d•Onsite

About The Position

AutoSavvy is a fast-growing automotive retailer focused on providing high-quality, branded title vehicles at competitive prices nationwide. We leverage data and internal systems to drive operational efficiency, pricing strategy, and decision-making across the business. We are looking for a Data Engineer to help scale our internal data and automation capabilities within a Microsoft Azure environment. This role focuses on building and maintaining data pipelines, improving reporting datasets, and developing internal tools that support operational pricing, and reporting decisions. You will be the first dedicated data engineering hire, helping define how data systems are built, maintained, and scaled across the organization, working directly with the technical lead responsible for architecture and strategy. This role is focused on execution, ownership, and building systems that scale. Our stack primarily includes Azure SQL, Python-based data workflows, Azure Functions and Container Apps for scheduled and event-driven workflows, and Azure Blob Storage. You will work across data pipelines, reporting datasets, and backend workflows. This role requires someone comfortable operating across multiple areas and building practical, scalable solutions.

Requirements

3-5 years of experience in data engineering or similar role
Ability to work independently on well-scoped problems with minimal guidance
Strong SQL skills (advanced querying, performance tuning, data transformations)
Proficiency in Python for data processing and automation
Experience writing maintainable, testable Python code
Experience using Git for version control (e.g., GitHub), including branching and pull request workflows
Hands-on experience with Azure data services, including: Azure SQL Database or SQL Server
Experience orchestrating data workflows (e.g., Azure Functions, Container Apps, Airflow, or similar)
Azure Blob Storage or Data Lake
Experience building and maintaining ETL/ELT pipelines
Experience working with large, structured datasets
Valid driver’s license with acceptable driving record
Ability to pass a background check
Authorized to work in the United States
Requirement of Multi-Factor Authentication apps on cell phone

Nice To Haves

Familiarity with data modeling for analytics and reporting
Experience integrating with REST APIs and external data sources
Understanding of CI/CD practices and tooling (Azure DevOps preferred)
Experience optimizing data workflows for cost and performance in Azure
Experience supporting Power BI through well-structured datasets and optimized data models
Proficiency with Excel for data analysis, validation, and ad hoc reporting
Experience with observability and monitoring (e.g., logging, metrics, alerting in Azure)
Curious and proactive in learning new tools, technologies, and industry practices
Stays current with modern data engineering and software development patterns
Comfortable leveraging AI-assisted development tools (e.g., Claude, Codex, ChatGPT, Grok, etc.) to improve productivity and solution quality
Able to critically evaluate AI-generated output and apply sound engineering judgment
Continuously looks for ways to improve systems, processes, and developer efficiency
Bias toward simple, pragmatic solutions over unnecessary complexity
Comfortable working independently with minimal oversight while aligning to defined priorities and architecture
Takes ownership of problems from initial concept through implementation and iteration
Proactively identifies gaps, inefficiencies, and opportunities for improvement
Communicates clearly on progress, tradeoffs, and blockers without needing constant direction
Comfortable asking for clarification when needed to ensure alignment and avoid misdirection

Responsibilities

Build, maintain, and optimize ETL/ELT pipelines using Azure services
Work with data across Azure SQL, Blob Storage, and related services
Ensure data quality, reliability, and performance through monitoring and troubleshooting
Implement data validation and testing (e.g., data quality checks, unit/integration tests) to ensure correctness and maintainability
Develop and maintain clean, reliable datasets for reporting and analytics
Collaborate on data models that support business metrics and dashboards
Write and optimize complex SQL queries for performance and clarity
Build Python-based scripts and services to automate internal workflows
Integrate with external APIs and internal systems
Reduce manual processes through automation
Execute against defined architecture and technical direction
Contribute to solution design
Communicate progress, blockers, and improvements clearly