Data Quality & Digital Twin Pk ECT Sces

The main role of the service holder is: Review tag registers, equipment lists, line lists, instrument indexes and related engineering datasets for completeness, accuracy and consistency. Validate tag numbers, functional classes, attributes, units, naming conventions and mandatory metadata against the AMIRAL project requirements and approved tagging rules. Identify duplicate, missing, invalid or inconsistent tags and coordinate correction with engineering disciplines and the contractor. Check alignment between structured data and source documents, including P&IDs, datasheets, lists, drawings and vendor information. Confirm that equipment and tag data can be linked correctly to documents and 3D model objects for Digital Twin use cases. Monitor data-quality KPIs such as completeness, validity, uniqueness, consistency and correction closure; issue clear dashboards and exception reports. Support data loading, validation and reconciliation in project information platforms such as Phusion or equivalent engineering data repositories. Work with engineering, Digital Twin, data management and contractor teams to resolve data issues before handover. Maintain an auditable data-quality issue log with owner, priority, due date, evidence and closure status. Contribute to data readiness reviews and verify that corrected datasets meet agreed handover criteria. Validated tag and asset datasets for the project. Weekly data-quality dashboard and prioritised error report. Data issue and correction follow-up register. Reconciliation results between datasets, engineering documents and 3D objects. Desired Candidate Profile 3 to 7 years of experience in engineering data, asset information, data quality, Digital Twin, information management or a similar industrial role. Good knowledge of technical English (written, spoken) Experience in oil and gas, petrochemical, refinery, energy, EPC or other asset-intensive projects. Practical understanding of engineering tags, equipment and piping data, document-to-tag relationships and structured metadata. Ability to analyze large datasets and identify quality gaps, patterns and inconsistencies. Good working knowledge of Microsoft Excel, including filters, lookups, pivot tables and data reconciliation. Power Query or Power BI is an advantage. Exposure to engineering information tools such as Phusion, Hexagon, Autodesk, SAP or equivalent platforms is an advantage. Clear communication skills and the ability to follow up with multidisciplinary engineering and contractor teams Bachelor degree or technical qualification in engineering, information systems, data management or a related field.