Senior Industrial Data Integration Engineer

Senior Industrial Data Integration Engineer

Full-Time 121500 - 148500 $ / year (est.) On-site
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At a Glance

  • Tasks: Design and build integrations for digital systems in manufacturing environments.
  • Company: Leading firm in industrial data integration with a focus on innovation.
  • Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
  • Other info: Dynamic role with significant impact on digital transformation in manufacturing.
  • Why this job: Join a cutting-edge team to shape the future of industrial data connectivity.
  • Qualifications: 6+ years in enterprise integration, experience with SAP and industrial protocols.

The predicted salary is between 121500 - 148500 $ per year.

Seniority: Senior (6+ years enterprise + industrial integration; OT/IT convergence essential) Team fit: Owns the integration layer and data model that feed the twin; the highest-risk workstream in the program.

About the role You will design and build the "digital thread" that connects the systems of record β€” SAP ERP, Tekla Power Fab, and the shop-floor MES (Dragon Forge) β€” together with real-time machine telemetry, into a canonical data model the digital twin consumes.

For new-build plants you will prove these integrations against sandbox systems and vendor controller emulators ahead of go-live; for existing plants you will connect to live production systems and equipment.

Key responsibilities

Design and build integrations to SAP ERP (orders, BOM, inventory), Tekla Power Fab (fabrication packages, piece/package tracking), and the MES (work-order execution, machine data).

Build shop-floor connectivity via OPC UA, MQTT, and PLC/SCADA data acquisition, including edge-to-cloud telemetry.

Design the canonical / semantic data model that maps enterprise and shop-floor data to twin objects (ISA-95-aware).

Stand up streaming and event pipelines (Kafka / Amazon MSK, Kinesis), a data lake, and master-data mapping on AWS.

Define integration and data standards early β€” including for greenfield systems still being configured β€” to avoid costly retrofits.

Work with equipment vendors (including controller emulators / OPC UA test servers) to validate connectivity before installation.

Partner with the OT/security teams on safe, compliant OT/IT convergence.

Required

Enterprise integration with SAP (IDoc / BAPI / RFC / OData) and MES/ERP APIs.

Industrial protocols: OPC UA, MQTT, Modbus; PLC / SCADA data acquisition.

Streaming / event-driven architecture (Kafka / MSK, Kinesis) and real-time data pipelines.

Data modeling, ETL/ELT, and data lake design on AWS.

Demonstrated OT/IT convergence work in a manufacturing or plant environment.

Preferred

Tekla Power Fab or structural-steel fabrication systems experience.

Unified Namespace (UNS) architecture and industrial Io T platforms.

Edge computing (AWS Io T Greengrass, industrial gateways).

OT cybersecurity and cross-border data-governance awareness.

Prior work feeding real-time data into a digital twin or 3D visualization layer.

Steel / metal fabrication or discrete-manufacturing domain knowledge.

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Senior Industrial Data Integration Engineer employer: Arcadion

NVIDIA is an exceptional employer that fosters a culture of innovation and collaboration, making it an ideal place for professionals passionate about shaping the future of manufacturing. With access to cutting-edge technology and a commitment to employee growth, you will have the opportunity to work on transformative projects in a dynamic environment. Located in a vibrant tech hub, NVIDIA offers competitive benefits and a supportive atmosphere that encourages creativity and professional development.

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Contact Details:

Arcadion Recruitment Team

We think you need these skills to ace Senior Industrial Data Integration Engineer

Enterprise Integration with SAP (IDoc / BAPI / RFC / OData)
MES/ERP APIs
Industrial Protocols (OPC UA, MQTT, Modbus)
PLC/SCADA Data Acquisition
Streaming/Event-Driven Architecture (Kafka / MSK, Kinesis)
Real-Time Data Pipelines
Data Modeling