Digital Factory Engineer IV leading projects on IIoT/Industry 4.0 technologies for Raytheon. Collaborating on projects from development to deployment, enhancing advanced manufacturing innovations.
Responsibilities
Lead projects focused on Ignition application development and deployment.
Advance IIoT initiatives by working with sensors, gateways, connectivity solutions, APIs, and data analytics platforms.
Recommend and implement methods to connect manufacturing equipment to IIoT/OT layer networks.
Configure communication protocols and network devices (e.g., EIP, Modbus/TCP, MQTT).
Collaborate on major team projects and independently solve complex SDF&A Engineering Systems problems.
Analyze large, complex data sets to support digital thread implementation and develop analytics reports and visualization tools.
Travel up to 10% of the time
Requirements
Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 8 years of prior relevant experience unless prohibited by local laws/regulations.
The ability to obtain and maintain a U.S. government issued Secret security clearance is required.
U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance
Experience with Ignition SCADA development with tools such as SQL, Python, C#/.NET, HTML5, and JSON.
Experience with Azure DevOps or similar Agile Project Management tools.
Demonstrated experience scripting, dashboarding, and developing APIs/web applications, with a minimum of 3 years of hands-on project work in these areas.
Proven experience applying Industry 4.0 concepts (e.g., IIoT, AI, machine learning, systems integration, ISA-95, or Pub/Sub) in projects such as predictive maintenance, real-time data collection, or manufacturing process optimization.
Practical experience with industrial communication protocols (e.g., Modbus/TCP, MQTT, EIP, SECS/GEM) in automated assembly lines, semiconductor manufacturing, or similar environments.
Experience with data analytics, including processing and visualizing data from multiple sources (e.g., sensors, ERP, MES) to drive operational improvements.
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