Torchora
Back to Home

Key Responsibilities and Required Skills for Digital Twin Specialist

💰 $ - $

Digital TwinIIoTSimulationEngineeringData

🎯 Role Definition

This role requires a Digital Twin Specialist — an experienced engineer who designs, builds, validates, and maintains digital twin solutions that mirror physical assets, processes, and systems to drive asset performance, predictive maintenance, process optimization, and data-driven decision making. The Digital Twin Specialist blends expertise in simulation (physics-based and data-driven), IIoT connectivity, cloud platforms (Azure/AWS/GCP), systems modelling (MBSE/SysML), data analytics and machine learning to deliver end-to-end digital twin products in manufacturing, energy, infrastructure or operations contexts.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Controls Engineer, IIoT Engineer, or Automation Engineer with PLC/SCADA experience
  • Simulation/CAE Engineer or CFD/FEA Engineer with asset modelling experience
  • Data Scientist / ML Engineer with time-series and industrial data exposure
  • Systems Engineer or Product Engineer with integration experience

Advancement To:

  • Senior/Lead Digital Twin Engineer or Digital Twin Architect
  • Head of Digitalization, Digital Transformation Lead, or Director of Digital Products
  • Principal Systems Engineer or Chief Data & Analytics Engineer

Lateral Moves:

  • IIoT/Edge Solutions Architect
  • Asset Performance Management (APM) / Reliability Engineer
  • Simulation & Modeling Specialist or MBSE Lead

Core Responsibilities

Primary Functions

  • Design, develop, validate and maintain end-to-end digital twin models (physics-based, data-driven and hybrid) for rotating equipment, process units, manufacturing cells or built environment assets to enable predictive maintenance, real-time monitoring and performance optimization.
  • Lead the selection and integration of IIoT telemetries, protocols (OPC UA, MQTT, Modbus), and edge devices to ensure reliable, secure streaming of sensor, PLC and SCADA data into cloud and on-premise digital twin environments.
  • Architect and deploy digital twin solutions on cloud platforms (Azure Digital Twins, Azure IoT Hub, AWS IoT TwinMaker, AWS IoT Core, GCP IoT) with scalable ingestion, processing and visualization pipelines.
  • Translate asset domain knowledge into formal models using MBSE/SysML, Simulink/Matlab, Modelica, or custom physics engines to represent behavior, failure modes and control interactions.
  • Build and operationalize machine learning and statistical models (time-series forecasting, anomaly detection, degradation models) to enable predictive analytics and condition-based maintenance strategies.
  • Implement model calibration, parameter identification and uncertainty quantification workflows using historical run-to-failure and test data to ensure model fidelity and explainability.
  • Design and implement real-time digital twin synchronization strategies—state estimation, sensor fusion, and digital-physical feedback loops—to keep the virtual replica aligned with the physical asset.
  • Integrate CAD/PLM/BIM data (e.g., Revit, SolidWorks, CATIA, Siemens Teamcenter) into the digital twin for spatial context, asset hierarchy and lifecycle traceability.
  • Develop APIs, microservices, and data contracts (REST, gRPC) to expose twin outputs to MES/ERP/maintenance systems, dashboards and third-party analytics tools.
  • Create visualization dashboards, KPIs and operator-facing UIs (Power BI, Grafana, custom web apps) to surface actionable insights, OEE improvements and prognostics to business stakeholders.
  • Lead cross-functional workshops with operations, maintenance, process and IT teams to capture requirements, validate model assumptions and prioritize twin features that deliver measurable ROI.
  • Implement data engineering best practices for feature engineering, time-series storage (InfluxDB, TimescaleDB), and data quality monitoring, including handling missing data and sensor drift.
  • Define lifecycle and governance processes for model versioning, CI/CD for models and code (Git, CI pipelines), and deployment of models to edge devices or cloud runtimes (Docker, Kubernetes).
  • Conduct root cause analysis and investigate anomalies flagged by the twin, producing reproducible reports and recommending corrective actions to engineering and maintenance teams.
  • Establish and monitor performance metrics for twins—prediction accuracy, latency, uptime, and business KPIs—and continuously iterate to improve model performance and operational value.
  • Lead pilot projects and proofs-of-concept, develop success criteria, estimate cost/benefit and present outcomes to technical and executive stakeholders to accelerate scaling.
  • Collaborate with cybersecurity and IT teams to design secure data flows, implement authentication/authorization, manage certificates for edge devices, and ensure compliance with OT/IT security policies.
  • Support commissioning and factory acceptance testing (FAT) activities, ensuring that models and integration components behave as expected under real-world operating conditions.
  • Mentor junior engineers, define best practices for modelling and code standards, and foster a knowledge-sharing culture for reuse of twin modules across asset classes.
  • Maintain documentation—model equations, data schemas, integration guides, runbooks and operator manuals—to ensure maintainability and transfer of domain knowledge.
  • Evaluate and recommend commercial digital twin platforms, simulation tools, and third-party SaaS solutions based on fit-for-purpose, extensibility and total cost of ownership.
  • Translate digital twin outputs into maintenance strategies, spare parts optimization, and lifecycle cost reduction plans with direct collaboration with reliability and procurement teams.
  • Drive continuous improvement by staying current with advances in physics-based modelling, ML for predictive maintenance, digital thread practices, and Industry 4.0 standards.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Assist procurement teams in developing technical specifications for sensors, gateways and edge computing hardware.
  • Provide training sessions for operations teams on how to interpret twin-derived diagnostics and recommended actions.
  • Participate in vendor evaluations and manage relationships with third-party platform and system integrators.
  • Support regulatory reporting and compliance activities where the digital twin provides audit trails or safety-critical analysis.
  • Help define and track KPIs tied to sustainability goals such as energy consumption reduction or emissions monitoring enabled by the twin.

Required Skills & Competencies

Hard Skills (Technical)

  • Digital twin architecture and lifecycle management (design, build, deploy, operate, retire) across industrial assets and processes.
  • Strong experience with IIoT protocols and OT integration: OPC UA, MQTT, Modbus, BACnet, DNP3, REST APIs.
  • Cloud platform expertise: Azure Digital Twins, Azure IoT Hub, AWS IoT TwinMaker/AWS IoT, Google Cloud IoT; experience designing secure cloud ingestion and processing pipelines.
  • Modelling & simulation: MATLAB/Simulink, Modelica, ANSYS, COMSOL, or equivalent physics-based modelling tools and CAE experience.
  • Data-driven modelling and ML for time-series: Python (pandas, scikit-learn, TensorFlow/PyTorch), R, and experience with forecasting, anomaly detection and prognostics.
  • MBSE and systems modelling: SysML, MagicDraw, Enterprise Architect or similar model-based systems engineering tools.
  • CAD/BIM/PLM integration skills: Revit, SolidWorks, Siemens NX/Teamcenter, Autodesk platforms and experience linking geometry to model logic.
  • Edge computing and device deployment: containerization (Docker), orchestration (Kubernetes), and experience deploying models to gateways/edge devices.
  • Databases and time-series storage: SQL, NoSQL, InfluxDB, TimescaleDB, Cassandra, experience with data ingestion and ETL pipelines.
  • SCADA/PLC integration, controls knowledge and familiarity with ladder logic, function block diagrams and control loop fundamentals.
  • API design, microservices, and backend development skills (Node.js, .NET, Java) for integrating twin services.
  • Version control and CI/CD: Git, Jenkins, Azure DevOps, GitHub Actions for automating model and application deployments.
  • Visualization and dashboarding: Power BI, Grafana, Tableau, or custom web UI development for operator and executive reporting.
  • Data governance, metadata management and unit/description standardization to ensure semantic consistency across twins.
  • Security best practices for OT/IT convergence, certificate management, secure firmware updates, and IAM for IoT solutions.

(At least 10 of the above hard skills are commonly required in real digital twin job postings.)

Soft Skills

  • Strong stakeholder management: translate business objectives into technical requirements and communicate model limitations clearly.
  • Cross-functional collaboration with operations, maintenance, IT, data science and procurement teams.
  • Problem-solving and systems thinking: decompose complex physical systems into maintainable virtual models.
  • Excellent written and verbal communication for technical documentation, executive briefings and training.
  • Project management and delivery focus: able to scope pilots, manage milestones and deliver measurable outcomes.
  • Critical thinking and data-driven decision making with an orientation toward ROI and operational impact.
  • Mentorship and knowledge-sharing to uplift team capabilities across modelling and IIoT disciplines.
  • Adaptability to evolving requirements and new protocols, tools and industrial standards.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Mechanical Engineering, Electrical Engineering, Systems Engineering, Computer Science, Data Science, or a closely related technical field.

Preferred Education:

  • Master's degree or higher in Systems Engineering, Mechanical/Process/Electrical Engineering, Data Science, Applied Mathematics, or a related discipline.
  • Certifications such as Certified Digital Twin Professional, Azure IoT Developer, AWS Certified IoT – Specialty, or MBSE/SysML training are advantageous.

Relevant Fields of Study:

  • Mechanical, Electrical, or Control Systems Engineering
  • Computer Science, Data Science or Applied Mathematics
  • Systems Engineering, Industrial Engineering
  • Simulation, Computational Physics or Mechatronics

Experience Requirements

Typical Experience Range: 3–8 years of professional experience in simulation, IIoT, digital twin development or related engineering roles.

Preferred:

  • 5+ years building or operating digital twin solutions, simulations, or IIoT integrations in industrial or infrastructure environments.
  • Demonstrated track record of delivering pilots to production, integrating SCADA/PLC data, and deploying models to cloud or edge with measurable business outcomes (e.g., reduced downtime, extended asset life, energy savings).
Explore More Careers