What are the responsibilities and job description for the Digital Twin Specialist | Construction position at Stradigi?
Job Description
Job Description:
The Digital Twin Lead is responsible for designing, developing, and managing digital twin solutions that simulate real-world systems, assets, or processes in a virtual environment. This role bridges the gap between operational technology (OT) and information technology (IT) by integrating IoT, analytics, and 3D modeling to enable predictive insights, real-time monitoring, and optimized decision-making. The Digital Twin Lead works closely with engineering, data science, and operations teams to lead digital twin strategies from concept to deployment.
Key Responsibilities
Job Description:
The Digital Twin Lead is responsible for designing, developing, and managing digital twin solutions that simulate real-world systems, assets, or processes in a virtual environment. This role bridges the gap between operational technology (OT) and information technology (IT) by integrating IoT, analytics, and 3D modeling to enable predictive insights, real-time monitoring, and optimized decision-making. The Digital Twin Lead works closely with engineering, data science, and operations teams to lead digital twin strategies from concept to deployment.
Key Responsibilities
- Strategy & Leadership:
- Define the digital twin architecture and roadmap aligned with enterprise goals.
- Lead cross-functional teams through the design and implementation lifecycle.
- Collaborate with business and technical stakeholders to identify use cases and opportunities.
- Solution Development:
- Develop and implement digital twin models for assets, systems, or processes.
- Integrate IoT data streams, CAD/3D models, real-time telemetry, and analytics.
- Use simulation and modeling techniques to represent real-world behavior digitally.
- Integration & Data Management:
- Integrate digital twins with IoT platforms, SCADA, ERP, PLM, and other enterprise systems.
- Manage data flow, connectivity, and synchronization between physical and virtual entities.
- Ensure data integrity, scalability, and compliance with data governance policies.
- Monitoring & Optimization:
- Enable real-time monitoring, diagnostics, and predictive analytics through digital twins.
- Implement AI/ML models for anomaly detection, scenario simulation, and optimization.
- Analyze KPIs and system behavior to recommend actionable improvements.
- Innovation & R&D:
- Stay current with advancements in digital twin technologies and best practices.
- Evaluate and implement emerging platforms, tools, and methodologies.
- Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, or related field.
- 5 years of experience in digital transformation, IoT, or simulation-related roles.
- Hands-on experience with digital twin platforms and tools.
- Strong understanding of systems modeling, industrial IoT (IIoT), and real-time data systems.
- Preferred certifications:
- Digital Twin Consortium, Azure IoT certifications, or similar
- Project management (PMP, Agile) or architecture certifications (TOGAF) are a plus
- Strong analytical and conceptual thinking.
- Ability to simplify complex systems and communicate ideas effectively.
- Collaborative leadership and cross-disciplinary coordination.
- Innovation mindset with a passion for digital transformation.
- Detail-oriented with a big-picture strategic approach.
- Digital Twin Platforms: Azure Digital Twins, Siemens NX, PTC ThingWorx, Ansys Twin Builder, GE Predix, Dassault Systèmes
- IoT & Edge Platforms: Azure IoT Hub, AWS IoT, Google Cloud IoT Core
- 3D Modeling & CAD: AutoCAD, SolidWorks, Unity, Unreal Engine
- Simulation & Analytics: MATLAB/Simulink, AnyLogic, Python, R
- Integration Tools: APIs, OPC UA, MQTT, REST, SCADA systems
- Data Platforms: Azure Data Lake, Azure Synapse, Snowflake, Time Series Insights
- Digital twin architecture and lifecycle management
- IoT data integration and sensor networks
- 3D modeling and simulation
- Systems thinking and real-time analytics
- Cloud infrastructure (Azure, AWS, GCP)
- Data science and machine learning
- Agile/DevOps methodologies
- Cyber-physical system modeling
- Predictive maintenance and operational efficiency