Data Engineer

Job DetailsJob Location: Woods Cross, UT 84087Position Type: Full TimeData Engineer (Mid-Level)
Utah (Local Required)

Overview

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.

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., 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

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 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)

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


Continuous