Data Engineer

Data Engineer (Mid-Level)
Utah (Local Required)
Overview Auto Savvy  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.

Scope of the Role 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.

What You'll Work On (Examples)
Optimize and extend existing pipelines improving reliability and reducing job runtimes while designing new pipelines and databases as needed

Build and maintain pipelines that ingest, transform, and standardize operational data Improve performance and reliability of SQL-based datasets

Automate internal workflows that require manual data handling

Design clean, reusable data models to support business metrics and dashboards

Integrate external APIs and internal systems into centralized data workflows

Responsibilities

Data Pipelines & Azure Infrastructure 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

Data Modeling & Reporting Support 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

Automation & Internal Tooling Build Python-based scripts and services to automate internal workflows

Integrate with external APIs and internal systems

Reduce manual processes through automation

Collaboration & Execution Execute against defined architecture and technical direction

Contribute to solution design

Communicate progress, blockers, and improvements clearly

Required Qualifications 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., Git Hub), 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

Preferred Qualifications 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 Dev Ops 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)
Mindset & Approach 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

Ownership & Working Style 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,…