IoT Integration Software Engineer

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., 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

• Hands-on experience with AWS IoT SiteWise, including asset models and hierarchies

• Experience with AWS IoT Greengrass or other edge-to-cloud integration platforms, * 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