Data Engineer

Data Engineer

Hot Job

Woods Cross, UT 84087

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
• 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, 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

What Success Looks Like

• Within the first 90 days, you will:
• Take ownership of existing Azure-based pipelines and improve reliability
• Deliver new data workflows with minimal oversight
• Reduce manual or repetitive processes through automation
• Improve performance and usability of reporting datasets
• Within 6-12 months, you will:
• Own a set of data pipelines or domains end-to-end
• Establish and improve standards for data quality, testing, and pipeline reliability
• Deliver measurable improvements in performance, maintainability, and operational efficiency
• Contribute to shaping best practices for how data systems are built and scaled
• What This Role is Not
• Not a narrowly scoped or ticket-driven position
• Not focused on building machine learning or AI model

Team & Growth

• You will work directly with the senior technical lead focused on system design and strategy
• You will have meaningful input into how solutions are implemented and improved
• Opportunity to grow into ownership of larger systems and architecture
• As systems scale, this role can expand into broader engineering or platform ownership
• Opportunity to mentor future hires and influence engineering and hiring standards as the team grows

Why This Role

• Be the first dedicated data engineering hire and help shape how data systems are built and scaled
• High-trust, low-bureaucracy environment with real ownership and decision-making autonomy
• Build systems that directly impact business operations, pricing, and reporting
• Focus on meaningful engineering work rather than one-off tasks

Physical Requirements

• Ability to sit at a desk for extended periods, perform repetitive tasks like typing, and frequent use of video calls.
• Dedicated Workspace: A quiet, private area free from noise and distractions to ensure productivity and data security.
• High-Speed Internet: Reliable broadband internet, often requiring a wired Ethernet connection to the router rather than Wi-Fi for