1

Automotive Data Engineer Jobs in Florida (NOW HIRING)

GBSI delivers technology-driven solutions across Manufacturing, Automotive, Life Sciences, Healt ... Bachelor s degree in Information Systems, Engineering, Computer Science, Business Administration ...

Manufacturing Engineer

Edgewater, FL · On-site

$68K - $88K/yr

Analyze production data to improve throughput, capacity utilization, Productivity improvement areas ... Fiberglass, marine, automotive, heavy equipment, or aerospace manufacturing. * Production launch ...

Quality Engineer

Miami, FL · On-site

$91.50/hr

Automotive OEM or Tier 1 supplier experience * Expertise in using quality management and ERP ... data extraction, analysis, and reporting. * ASQ Certification (e.g., Certified Quality Engineer ...

Sr Systems Test Engineer

Orlando, FL · Hybrid

$97K - $133K/yr

MicroVision's core technology, initially developed for the automotive industry, continues to ... Develop automated test scripts, tools, and data analysis frameworks.   * Improve test ...

MicroVision's core technology, initially developed for the automotive industry, continues to ... Analyze large data sets generated during system testing. * Generate technical reports and present ...

MicroVision's core technology, initially developed for the automotive industry, continues to ... Analyze large data sets generated during system testing. * Generate technical reports and present ...

Vehicle Quality Engineer

Jacksonville, FL · On-site

$70K - $91K/yr

... data, and parts manuals * Support validation/testing activities as needed (fit/function checks ... automotive, or a related vehicle/equipment industry * Demonstrated success leading root-cause ...

Analyze characterization data and provide technical insights related to product performance, margin ... for Automotive, Industrial, Infrastructure, and IoT, shaping a safer, healthier, greener, and ...

Showing results 21-40

Automotive Data Engineer information

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 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 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 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 cities in Florida are hiring for Automotive Data Engineer jobs? Cities in Florida with the most Automotive Data Engineer job openings:
Infographic showing various Automotive Data Engineer job openings in Florida as of August 2026, with employment types broken down into 85% Full Time, and 15% Contract. Highlights an 100% In-person job distribution.

Other

Re-posted 29 days ago


Job description

Job description Company Description Dealer Automation Technologies is a leading Information Technology Services company that provides Software as a Service (SaaS) solutions for the automotive industry. The company specializes in Process Automation, Advanced Analytics, and Augmented Intelligence to redefine dealership operations. With a focus on intuitive user experiences and cutting-edge technologies, Dealer Automation Technologies is paving the way for innovation in the sector.

Combining the agility of a startup with the expertise and business acumen of seasoned leaders, the company operates without dependence on legacy systems, fostering a dynamic and innovative work culture. Role Description This is a full-time, on-site role located in Miami, FL, for a Senior AI/ML Engineer specializing in Large Language Models (LLMs) to join our team. You will play a key role in designing and implementing workflows that leverage large language models (LLMs, LAMs, LMMs, LVLMs, etc.) to automate processes and drive innovation in our products

The ideal candidate will have a deep understanding of NLP, experience with foundational models, and a flexible, problem-solving mindset. You will collaborate closely with cross-functional teams, contributing to the development of scalable AI driven solutions. Other primary responsibilities include designing and implementing machine learning models, particularly in natural language processing and large language models, building scalable algorithms, conducting research on neural networks, and evaluating model performance.

Additionally, the engineer will collaborate with cross-functional teams to ensure seamless integration of AI/ML components into the company's software offerings. Major Areas of Responsibility Design, Implement, and optimize workflows that incorporate large language models to automate and enhance product features. Leverage existing foundational models and adapt them to fit into various product requirements, ensuring alignment with business goals.

Collaborate with product managers, data scientist, and software engineers to integrate LLM-based automation into scalable solutions. Create and architect interpreters, Agented Systems, and integrate multi-hop RAG and other LLM experiences into existing systems to coordinate knowledge responses. Research and evaluate new technologies and methodologies in the LLM space to continuously improve product automation.

Work on the customization and fine-tunning of models to optimize performance for specific use cases. Develop, test, and deploy LLM-based services in production environments. Provide AI/ML technical leadership and mentorship to other engineers on the team.

Ensure that LLM integrations are efficient, scalable, and secure, adhering to industry best practices.