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Automotive Data Engineer Jobs in Arizona (NOW HIRING)

Staff Data Engineer

Tempe, AZ · On-site

$140 - $210/hr

RevolutionParts is not just a pioneering force in the automotive eCommerce realm; we're actively ... The Role Most data engineering roles hand you a Jira board. This one hands you a whiteboard and ...

Senior Data Engineer, Predictive Modeling

Tempe, AZ · On-site

$101K - $137K/yr

... automotive retailer in history. We expanded nationally, went public on the New York Stock Exchange ... We're not just building data pipelines; we're engineering intelligent systems that predict the ...

... automotive retailer in history. We expanded nationally, went public on the New York Stock Exchange ... We're not just building data pipelines; we're engineering intelligent systems that predict the ...

... automotive retailer in history. We expanded nationally, went public on the New York Stock Exchange ... We're not just building data pipelines; we're engineering intelligent systems that predict the ...

Data Engineer / BI Developer

Phoenix, AZ · On-site +1

$70K - $80K/yr

... defense, automotive, energy, hi-tech, healthcare, medical devices, rail and semiconductor ... Build and maintain the data pipeline supporting traceability and status reporting * Develop ...

As a Texas Instruments Product Engineer, you will have the opportunity to work in a vibrant and ... automotive, data center, personal electronics and communications equipment. At our core, we have a ...

As a Texas Instruments Product Engineer, you will have the opportunity to work in a vibrant and ... automotive, data center, personal electronics and communications equipment. At our core, we have a ...

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Automotive Data Engineer information

What is an automotive data engineer?

An Automotive Data Engineer is a professional who designs, develops, and manages systems for collecting, processing, and analyzing data generated by vehicles and automotive systems. They work with large datasets from sources such as sensors, telematics, and onboard diagnostics to improve vehicle performance, safety, and efficiency. Their role often involves collaborating with software developers, data scientists, and automotive engineers to build data-driven solutions for connected and autonomous vehicles.

What key skills and qualifications are needed to thrive as an automotive data engineer?

To thrive as an Automotive Data Engineer, you need expertise in data analytics, programming (often Python or SQL), and a strong understanding of automotive systems, typically supported by a degree in computer science, engineering, or a related field. Familiarity with big data platforms (like Hadoop or Spark), automotive communication protocols (such as CAN or LIN), and certifications in data engineering or cloud technologies are highly valued. Strong problem-solving abilities, teamwork, and effective communication help distinguish top performers in this role. These skills are crucial for developing reliable data-driven solutions that enhance vehicle performance, safety, and innovation in a rapidly evolving automotive industry.

What are some common challenges automotive data engineers face when working with vehicle data?

Automotive Data Engineers often encounter challenges related to the sheer volume and complexity of data generated by modern vehicles, including sensor, telematics, and diagnostic information. Integrating data from various sources and ensuring its quality, consistency, and security can be demanding. Additionally, collaborating with cross-functional teams—such as software developers, data scientists, and automotive engineers—requires strong communication skills to align technical requirements and project goals. Adapting to evolving automotive technologies and compliance standards is also crucial for success in this role.

What is the difference between Automotive Data Engineer vs Data Scientist in the automotive industry?

AspectAutomotive Data EngineerData Scientist
Required CredentialsBachelor's in Computer Science, Data Engineering, or related field; experience with SQL, Python, big data toolsBachelor's or Master's in Data Science, Statistics, or related; proficiency in Python, R, machine learning
Work EnvironmentAutomotive companies, tech firms, data infrastructure teamsResearch labs, automotive R&D, analytics teams
Employer & Industry UsageFocus on building data pipelines, managing data infrastructure in automotive settingsFocus on analyzing data, creating models for vehicle performance, customer insights

Automotive Data Engineers primarily develop and maintain data infrastructure within the automotive industry, ensuring data flows efficiently. Data Scientists analyze this data to generate insights and predictive models. Both roles often collaborate but focus on different aspects of data management and analysis.

What are popular job titles related to Automotive Data Engineer jobs in Arizona?

For Automotive Data Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Automotive Data Engineer jobs in Arizona look for?

The top searched job categories for Automotive Data Engineer jobs in Arizona are:

What cities in Arizona are hiring for Automotive Data Engineer jobs?

Cities in Arizona with the most Automotive Data Engineer job openings:

Staff Data Engineer

Tempe, AZ • On-site

Socket.dev
Network Security • 1 - 10 employees

$140 - $210/hr

Other

Retirement

Posted 8 days ago


Job description

RevolutionParts is not just a pioneering force in the automotive eCommerce realm; we're actively seeking passionate and talented individuals to join our squad of Revolutionaries (yes, that's what we call ourselves!). As leaders in providing streamlined, user-friendly solutions, we empower automotive brands to maximize online sales. Our commitment to technology, top-notch customer service, and a profound understanding of the automotive market sets us apart. If you're ready to revolutionize the eCommerce space for automotive parts and accessories, consider joining our dynamic team of Revolutionaries.

The Role

Most data engineering roles hand you a Jira board. This one hands you a whiteboard and asks what should be on it.RevolutionParts powers parts and accessories commerce for thousands of automotive dealers and OEMs across North America. The data behind all of it (catalog, pricing, inventory) moves through a high-volume ingestion system that has scaled with the business. It was the right architecture for where we were. It isn’t the right architecture for where we’re going.

Responsibilities Strategic Leadership & Architectural Ownership
  • You are the technical authority for data ingestion at RevolutionParts. You lead through expertise, not authority.
  • Own the 2-3 year architectural vision for data ingestion. That means the destination, the migration sequence, the tradeoffs at each stage, and the criteria that determine when the current system has earned its retirement.
  • Set the engineering standards that govern how every team builds on and interfaces with core data infrastructure: schema design, data contracts, query optimization, observability. What you establish here becomes the organization’s baseline.
  • Shape technical strategy across Product, BI, Platform Engineering, and Executive Leadership. Not as an advisor. As the person who drives alignment, cuts through ambiguity, and owns the outcomes of complex multi-quarter initiatives from discovery through delivery.
  • Take ownership of the highest‑severity, most ambiguous problems in the data domain: the ones that cross team boundaries, have no clear owner, and have already resisted resolution.
Execution & Operational Excellence
  • Hold ultimate accountability for the architecture and production performance of our catalog, pricing, and inventory ingestion systems, with the technical depth to make decisions no one else in the organization is positioned to make.
  • Define the reliability bar for data across the organization. Build the monitoring, alerting, and validation frameworks that turn data quality from a best‑effort into a contractual commitment with clear SLAs and owners.
  • Make final, binding technical debt decisions for the ingestion domain, weighing immediate stability against long‑term architectural health. Document the reasoning with enough clarity that it survives organizational change 18 months from now.
  • Elevate the technical ceiling of the data engineering organization through direct mentorship of Senior Engineers on distributed systems, high‑volume database performance, and data modeling at scale. Your impact here compounds beyond your own output.
Requirements

10+ years in data or software engineering, at least 3 at Staff level or equivalent owning architectural decisions on high‑volume production systems.

Python and Spark/PySpark at petabyte scale — production systems, not notebooks. You tune Spark from first principles: partition strategy, join optimization, dynamic allocation, skew diagnosis.

  • Designed and operated distributed job execution systems: dynamic compute provisioning, variable workload profiles, job isolation, and resource contention at scale.
  • Deep experience with message queue architectures in production: fan‑out patterns, poison pill handling, dead letter queues, consumer lag at scale.
  • Built observability into systems that had none — monitoring, alerting, lineage, and pipeline health designed in from the ground up, not dashboards bolted on afterward.

Built pipeline orchestration infrastructure, not just DAGs. You have strong opinions about operability because you've inherited systems that weren't.

  • You set the engineering quality bar. Reliable, efficient, documented, testable, maintainable — and you hold the team to the same standard.

Led a migration from legacy batch infrastructure (custom schedulers, daemon‑based systems, cron pipelines) to modern architecture without taking down production. We'll go deep on this in the interview.

  • Deep AWS in production: EKS, EC2 fleet management, SQS, RDS. Operated at scale, not just deployed into it.
  • Kubernetes in production — workload behavior, compute right‑sizing for variable job profiles, failure modes under load.
  • Streaming in production: Kafka, Flink, Kinesis, or Redpanda. You've made the batch‑vs‑streaming call in both directions and can defend either.
  • Cloud data warehouse architecture — Snowflake, BigQuery, or Databricks. Clustering, partitioning, cost management, mixed analytical and operational workloads.
  • You use AI coding tools daily and have shipped production work because of it.
  • You write architecture docs engineers trust and can brief a VP on the same decision. Both matter at this level.
  • BS or MS in Computer Science, Engineering, or equivalent.
AI Fluency & Modern Tooling
  • Using AI tools responsibly to accelerate research, analysis, documentation, and problem-solving
  • Exercising strong judgment around data privacy, accuracy, and ethical use
  • Continuously learning and adapting as AI capabilities evolve

Proven examples of using AI to improve outcomes in prior roles is expected.

RevolutionParts is proud to provide all full‑time Revolutionaries with a comprehensive employment package including competitive compensation, career development, benefits, 401K match, parental leave, and many more valuable perks. You can learn more about our core‑value driven culture at our career page.

RevolutionParts is an Equal Opportunity Employer; we value diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, gender orientation, gender identity or expression, sexual identity, sexual orientation, age, marital status, family status, genetic information, veteran status, or disability status.

Please Note:

You will only receive correspondence through the GEM ATS or from a @revolutionparts.com email address. If you are receiving communication through any other platform or domain, it may be fraudulent, and we urge you to ignore the communication.

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