Job Title - IoT Integration Software Engineer Digital Products Locations: Cary, NC / Overland Park, KS (Remote) *Locals only*
Hands-on experience with AWS IoT SiteWise, including asset models and hierarchies
Experience with AWS IoT Greengrass or other edge-to-cloud integration platforms
Role Overview
We are seeking an experienced IoT Integration Software Engineer to join the Digital & IT organization . This role focuses on the ingestion, modeling, and integration of industrial telemetry data into modern, cloud-native digital products. You will serve as a subject-matter expert for IoT data ingestion and asset modeling, working closely with application engineers, hardware integration engineers, and product teams to ensure that telemetry data is reliable, well-structured, and usable by downstream applications. This role emphasizes IoT domain knowledge, cloud-native integration, and bridging physical assets to digital systems.
The Team This role sits within the D&IT Digital Products team, which enables people, projects, and businesses through modern platforms, data, analytics, and digital products. Our teams build and operate software using agile, product-oriented ways of working with a strong focus on quality, security, and reliability, and long-term maintainability.
Key Responsibilities
As an IoT Integration Software Engineer, you will:
Design, develop, and maintain IoT ingestion pipelines from edge devices to cloud platforms
Serve as a technical expert for AWS IoT SiteWise, including asset models, hierarchies, and data semantics
Work with AWS IoT Greengrass and edge-based integrations to support secure, reliable data ingestion
Collaborate with hardware and OT integration engineers to perform asset mapping between physical devices and digital models
Develop tooling, validation, and diagnostics to ensure telemetry quality and correctness
Partner closely with application engineers to expose IoT data through APIs, event streams, and application-ready interfaces
Translate industrial and operational concepts into data structures usable by digital applications
Support troubleshooting and root-cause analysis for ingestion and telemetry issues across environments
Contribute to documentation and shared standards related to asset modeling, ingestion patterns, and data contracts
Stay current on emerging IoT, telemetry, and cloud-native integration patterns
This role is an individual contributor position with strong domain ownership and cross-team influence. What Success Looks Like (First 6 12 Months)
Successfully supporting production ingestion pipelines for industrial telemetry
Establishing clear, maintainable asset models aligned to physical systems
Reducing ingestion-related defects and data quality issues for application teams
Becoming a trusted SiteWise and IoT integration SME for product engineers
Improving visibility and diagnostics for asset mapping and ingestion failures
Enabling application teams to move faster by abstracting IoT complexity
Job Description Minimum Qualifications
Bachelor s degree in computer science, Engineering, or a related field, or equivalent practical experience
4 7 years of professional software development or integration experience
Strong proficiency in at least one backend language (e.g., Python, TypeScript)
Experience working with cloud-based IoT or telemetry systems
Understanding of event-driven or streaming data architectures
Experience collaborating across software, platform, and hardware-adjacent teams
Familiarity with Git-based version control and collaborative development workflows
Preferred Qualifications
Hands-on experience with AWS IoT SiteWise, including asset models and hierarchies
Experience with AWS IoT Greengrass or other edge-to-cloud integration platforms
Experience working with industrial IoT or operational technology (OT) environments
Exposure to RTACs, DCIM systems, or industrial control telemetry
Experience with time-series data and telemetry ingestion patterns
Familiarity with containerized applications and cloud-native deployment models
Experience integrating IoT data into web or SaaS-style applications
Exposure to GenAI / LLM techniques applied to diagnostics, metadata enrichment, or operational insights
Understanding of data validation, observability, and ingestion monitoring practices
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