Automation & Data Integration Engineer (PLC & Cloud)

Kendall Howard•Chisago City, MN

About The Position

We are looking for a versatile Automation & Data Integration Engineer who can bridge the gap between machine-level control and high-level data insights. This role is responsible for developing the PLC logic that runs our products while simultaneously building the cloud-based ecosystem for data retrieval, analytics, and reporting. You will be the primary architect for how our machines "talk" to the cloud, ensuring that the data captured on the factory floor or in the field is transformed into actionable reports for our teams and customers.

Requirements

The "Hybrid" Skill Set: PLC Side: Proven experience with major platforms (e.g., Allen-Bradley/Rockwell, Siemens, or Beckhoff) and HMI design.
Cloud/Dev Side: Proficiency in programming languages used for data handling and web apps (e.g., Python, JavaScript/Node.js, or SQL).
Networking Knowledge: Strong understanding of industrial networking protocols (Ethernet/IP, Modbus, MQTT) and how to securely pass data through firewalls to cloud endpoints.
Hands-on Grit: This is a "doing" role. You should be comfortable moving from a laptop at a desk to a control panel on the shop floor.
Problem Solver: A track record of taking messy, raw machine data and turning it into clean, readable reports.

Responsibilities

Logic Design: Develop, program, and test PLC software for custom and integrated EV systems, ensuring high reliability and safety.
Hardware Integration: Partner with Electrical Engineering to ensure code aligns perfectly with wiring diagrams, I/O layouts, and control architectures.
Commissioning: Lead the debugging, troubleshooting, and validation of systems during prototype builds and factory acceptance testing.
Edge-to-Cloud Connectivity: Develop and implement the protocols (MQTT, OPC-UA, or similar) to move data from the PLC to cloud environments.
App Development: Build and maintain a cloud-based application or dashboard for real-time data retrieval, performance monitoring, and historical reporting.
Database Management: Structure how machine data is stored and indexed to ensure fast, reliable reporting and long-term scalability.
Visualization: Design user-friendly interfaces and automated reports that allow internal teams and customers to track product health and performance.
Predictive Insights: Collaborate with the Service team to use retrieved data for proactive troubleshooting and predictive maintenance alerts.
Remote Support: Use your data architecture to provide remote diagnostics and software updates for systems deployed in the field.
Continuous Improvement: Capture data-driven "lessons learned" to standardize programming practices and improve future product iterations.
Documentation: Maintain rigorous version control for both PLC code and cloud-app iterations, including change logs and user manuals.