Data Engineer – Physical AI Platform

Caterpillar Inc.•Irving, TX

1d•$97,530 - $158,480•Onsite

About The Position

At Caterpillar, technology always has a purpose, which is to solve our customers’ toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world—on real jobsites, at global scale. You’ll build and deploy against one of the most unique data foundations—over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale. Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible—intelligent machines operating in dynamic jobsites—into reality. Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against. If this work motivates you, we invite you to join our team. In these roles, you’ll work at the intersection of the physical and digital worlds. You’ll help design and deliver intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations. Apply today to build the new era of construction autonomy at Caterpillar.

Requirements

Decision Making and Critical Thinking: Ability to analyze issues in distributed data systems and deliver scalable solutions
Effective Communication: Ability to document data flows, mappings, and system behavior clearly for cross-team use
Software Development: Experience building backend systems and pipelines using Python, Java, and modern frameworks
Software Development Life Cycle: Experience delivering solutions in an agile environment
Software Integration Engineering: Experience integrating APIs, streaming platforms, and databases
Software Product Design/Architecture: Ability to design scalable, event-driven data systems
Software Product Technical Knowledge: Strong understanding of AWS services and data engineering tools
Software Product Testing: Experience implementing testing strategies to ensure data quality and system reliability

Nice To Haves

Bachelor’s degree in Computer Science, Computer Engineering, or related field
2+ years development experience on modern, large scale, complex data platforms
3+ years developing and deploying Java or Python solutions to a production environment
Experience building high-throughput, scalable data pipelines
Strong hands-on experience with AWS data services (Kinesis, S3, DynamoDB, EventBridge, etc.)
Proficiency in SQL, including data quality and validation practices
Proficiency in deploying software using CI/CD tools such as Azure DevOps, Jira, Jenkins, etc.
Experience developing microservices that support real-time data ingestion
Experience developing software applications using relational and noSQL databases
Ability to ensure data integrity across distributed and streaming systems
Experience with API development and secure integrations (OAuth 2.0)
Proven experience with monitoring, testing, and automation in large-scale data environments

Responsibilities

Design, build, and optimize data pipelines and microservices using Python for real-time and batch processing
Develop cloud-based data ingestion solutions using AWS services (Kinesis, S3, DynamoDB, EventBridge, etc.)
Build and maintain data integration workflows for source data pipelines aligned to CI Autonomy
Translate business requirements into reliable data workflows, mappings, and system designs
Implement automated testing and validation to ensure data integrity across distributed systems
Monitor and troubleshoot pipelines using tools like CloudWatch to maintain reliability