Job Title -IoTIntegration Software Engineer

Job Title -IoT Integration Software Engineer Locations: Cary, NC / Overland Park, KS / Remote (US) need to go monthly once Duration : Longterm Mandatory Skills : Aws Greengrass Aws Sitewise 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 Thanks :