Overview
We are looking for an experienced Automation Engineer to contribute to a significant control system migration and upgrade study for an operational tank farm facility. This role involves collaborating with operations teams, control systems specialists, and project stakeholders to evaluate and enhance existing automation and gauging systems. The ideal candidate will leverage their extensive experience in automation within process industries to develop effective migration strategies and provide technical recommendations for future development phases.
Responsibilities
- Interface with Operations teams and Control Systems specialists to capture stakeholder requirements.
- Review existing and future I/O requirements across the facility.
- Conduct loop-by-loop assessments with Operations teams to validate functionality and identify optimization opportunities.
- Develop migration and control system upgrade strategies for ageing automation systems.
- Assess control system architecture options, including migration and re-architecture approaches.
- Support development of cutover and transition strategies, minimizing operational disruption.
- Document engineering studies, technical decisions, risks, and recommendations.
- Prepare final reports and presentations for project funding and approval stages.
Requirements
- Degree-qualified in Automation, Control Systems, Electrical Engineering, or related discipline, or equivalent industry experience.
- Extensive experience in Automation Engineering within process industries such as refining, petrochemical, and oil & gas.
- Proven track record in delivering control system migration, upgrade, or modernization studies.
- Strong understanding of DCS, PLC, SCADA, and industrial control systems.
- Experience in both engineering project environments and owner/operator organizations.
- Knowledge of brownfield project execution and operational continuity planning.
- Excellent skills in stakeholder engagement and workshop facilitation.
- Ability to work independently and manage technical workstreams with minimal supervision.