Role summary
We’re hiring a hands-on OT Consultant to build, connect, and operationalize industrial data flows from shop-floor systems (PLC/SCADA/DCS, sensors, historians) through edge gateways into OT/IIoT platforms and cloud data stacks. This role is “on the tools hands on delivery”: configuring connectivity, building pipelines, deploying edge apps, validating data quality, and supporting commissioning/testing on live factory environments.
You’ll work with platforms like ThingWorx, Litmus, Ignition, Sight Machine, Mendix, and integration tools like Node-RED, plus modern cloud services (Azure/AWS) and industrial protocols (OPC UA/MQTT). You’ll be a key delivery driver in global multi-site rollouts.
________________________________________
Key responsibilities
OT connectivity & data acquisition (shop-floor to edge)
• Connect to OT sources: PLCs, SCADA, DCS, industrial sensors, historians, and machine controllers.
• Configure and troubleshoot industrial connectivity using protocols such as OPC UA/DA, MQTT (Sparkplug B), Modbus TCP/RTU, EtherNet/IP, PROFINET, and REST APIs (where applicable).
• Implement tagging standards and support tag governance (naming conventions, units, sampling, quality flags, timestamps).
Edge engineering & local orchestration
• Deploy and configure edge runtimes and gateways (on industrial PCs/VMs):
o Litmus Edge, Ignition Edge, Kepware/KEPServerEX, HiveMQ/Mosquitto, Azure IoT Edge / AWS Greengrass (as relevant)
• Build edge workflows for buffering, filtering, enrichment, and routing using:
o Node-RED, Python, scripting, connectors, store-and-forward patterns
• Ensure resilient edge operations: offline buffering, retry logic, local persistence, health monitoring.
OT/IIoT platforms configuration & app enablement
• Configure and develop solutions within OT/IIoT platforms such as:
o PTC ThingWorx (modeling, connectivity, mashups/services)
o Sight Machine (data onboarding, model alignment, analytics enablement)
o Inductive Automation Ignition (tags, gateways, OPC/MQTT modules, UIs)
o Litmus (connectors, pipelines, edge analytics)
o Mendix (lightweight apps/workflows on top of OT data)
• (Optional/nice to have depending on stack) Support configuration for:
o AVEVA (System Platform / Historian / PI-like patterns), Siemens Industrial Edge / MindSphere, Tulip, HighByte Intelligence Hub, Kepware, OSIsoft PI / AVEVA PI System
Ingestion & pipelines (edge > OT platforms > cloud)
• Implement end-to-end ingestion pipelines:
o From L0–L2 signals (machines) > edge > OT platform > cloud ingestion
• Validate payload formats, schemas, batching, compression, and frequency to meet performance needs.
• Support cloud ingestion patterns using:
o Azure (IoT Hub/Event Hubs, Functions, Data Lake, Databricks/Synapse)
o AWS (IoT Core/Kinesis, Lambda, S3/Glue)
o Integration into Snowflake (via streaming/batch connectors where relevant)
• Implement data quality checks: completeness, duplication, timestamp drift, unit consistency, gap detection.
Site delivery, commissioning & troubleshooting
• Work on-site (or remote to site edge systems) to support:
o Installation, configuration, commissioning, and integration testing
o Firewall/network coordination with Site IT (ports, routes, certs, proxies)
• Troubleshoot connectivity and performance across the full chain (OT > edge > platform > cloud).
• Document configurations, runbooks, and handover packages for operations/support teams.
________________________________________
Required qualifications & experience
• 4–8+ years in OT/industrial integration roles with hands-on delivery in manufacturing environments.
• Strong practical experience with at least two of the following:
o ThingWorx, Ignition, Litmus, Sight Machine, Mendix
• Proven experience building integration flows using Node-RED (or equivalent low-code orchestration) plus scripting (Python preferred).
• Solid understanding of OT systems and protocols (OPC UA, MQTT, Modbus, etc.) and the realities of plant networks.
• Comfortable working with multiple stakeholders (OT, IT, Security, OEMs, System Integrators) in a global environment.
________________________________________
