Lead Data Engineer – Physical AI Platform, Data Engineering

About the Role

At Caterpillar, technology always has a purpose—solving our customers’ toughest challenges. Through Cat Technology, you’ll build 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, you’ll deliver solutions that work in the real world on real jobsites at global scale.

You’ll work at the intersection of the physical and digital worlds, designing and delivering intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations. This role focuses on construction autonomy, one of the most complex challenges in applied AI, leveraging advancements in physical AI, simulation, sensing, and edge computing.

As a Lead Data Engineer, you will design, build, and maintain scalable data pipelines, microservices, and cloud-based data platforms that deliver reliable, high-quality data for business and engineering teams. You’ll work in an agile environment, driving data architecture, performance, reliability, and continuous improvement across modern data solutions.

Responsibilities

• Actively collaborate with Principal Software Engineers and Data Architects to define solution architecture.
• Lead the solution design and optimization of scalable data pipelines and microservices in Python, enabling both real-time and batch data processing across enterprise platforms.
• Drive the development of cloud-native data ingestion and streaming solutions leveraging AWS services including Kinesis, S3, DynamoDB, EventBridge, and related technologies.
• Own the design, implementation, and operational excellence of data integration frameworks and source data pipelines supporting CI Autonomy initiatives.
• Partner with business, product, and engineering stakeholders to translate complex requirements into scalable data architectures, workflows, mappings, and system designs.
• Establish and enforce automated testing, data quality controls, and validation frameworks to ensure integrity, reliability, and compliance across distributed data ecosystems.
• Lead operational monitoring, performance tuning, and root-cause analysis of production data platforms using observability tools such as CloudWatch to maintain high availability and service reliability.

Requirements

• Bachelor’s degree in Computer Science, Computer Engineering, or related field.
• 8+ years of experience in data engineering or related disciplines with increasing responsibility.
• Extensive experience on modern, large-scale, complex data platforms such as Caterpillar’s Helios Data Platform.
• Strong foundation developing and deploying Python solutions to a production environment.
• Experience leading teams to build high-throughput, scalable data pipelines.
• Strong hands-on experience with AWS data services (Kinesis, S3, DynamoDB, EventBridge, etc.) at scale.
• Proficiency in SQL, including data quality and validation practices.
• Experience 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 monitoring, testing, and automation in large-scale data environments.

Skills

• Decision Making and Critical Thinking: Ability to lead the analysis and resolution of complex issues within distributed data platforms, designing scalable and resilient solutions.
• Effective Communications: Ability to communicate across teams by sharing feedback constructively, listening to others, and creating documentation that makes data systems and processes easy to understand and support.
• Software Development: Experience leading the design and development of backend systems and data pipelines using Python, Java, and modern frameworks, providing technical direction and ensuring reliable, scalable solutions.
• Software Development Life Cycle: Experience leading the delivery of data engineering solutions in an Agile environment by guiding work through the full development lifecycle, translating requirements into technical solutions, and ensuring projects are delivered with quality and business value.
• Software Integration Engineering: Capability to lead the design and integration of APIs, data pipelines, streaming platforms, and databases to enable reliable data exchange across enterprise systems and partner platforms.
• Software Product Design/Architecture: Expertise leading the design of scalable, event-driven data systems and architectures, guiding technical decisions and ensuring solutions are reliable, maintainable, and aligned with business needs.
• Software Product Technical Knowledge: Ability to apply strong knowledge of AWS services and data engineering tools to define requirements, support testing and deployment activities, troubleshoot issues, and ensure data solutions are configured and operated effectively.
• Software Product Testing: Ability to define and implement testing strategies, including functional, performance, and data quality testing, to ensure reliable, scalable, and high-performing data solutions.

Benefits

• Medical, dental, and vision benefits (subject to plan eligibility, terms, and guidelines).
• Paid time off plan (Vacation, Holidays, Volunteer, etc.).
• 401(k) savings plans.
• Health Savings Account (HSA).
• Flexible Spending Accounts (FSAs).
• Health Lifestyle Programs.
• Employee Assistance Program.
• Voluntary Benefits and Employee Discounts.
• Career Development.
• Incentive bonus.
• Disability benefits.
• Life Insurance.
• Parental leave.
• Adoption benefits.
• Tuition Reimbursement.

These benefits also apply to part-time employees.

Pay

Summary pay range: $128,470.00 – $208,770.00. Compensation and benefits may vary depending on job level, market location, job-related knowledge, skills, individual performance, and experience.

Schedule

This position requires working onsite five days a week in the Chicago, IL office. Domestic relocation assistance and visa sponsorship are available for eligible applicants.