Business Data Architect

Power International Holding is seeking an experienced Data Engineer / Data Architect to design, develop, and maintain a reliable, governed data platform supporting enterprise project reporting and decision-making across the Group. This role will be responsible for consolidating project, cost, and schedule data from enterprise systems, including SAP, Primavera P6, CPCO, and DAMAS, into a unified, validated data model. The focus is on automating data integration, strengthening data quality, and ensuring every reported metric is traceable to its source. Key Responsibilities Data Architecture and Modelling • Design and maintain dimensional data models, including fact tables, dimensions, and semantic layers for enterprise reporting. • Establish data architecture standards covering naming conventions, transformation logic, version control, and change management. ETL Pipeline Development • Build and maintain automated ETL pipelines integrating data from SAP, Primavera P6, CPCO, DAMAS, and external submissions. • Implement scheduled refreshes, incremental loading, orchestration, failure alerts, and recovery procedures. • Develop robust pipelines that identify source schema changes and data inconsistencies rather than allowing silent failures. Primavera P6 Integration and Schedule Analytics • Extract and transform Primavera P6 data, including activities, work breakdown structures (WBS), resources, and costs. • Develop datasets and calculations supporting schedule variance, float, critical path analysis, progress curves, and forecast trends. Data Quality, Governance, and Lineage • Implement reconciliation controls between source systems and reports to identify discrepancies before publication. • Establish data validation rules, quality checks, and exception reporting. • Maintain end-to-end data lineage documentation, ensuring every reported metric can be traced from its source through transformation logic to the final output. Reporting and AI Enablement • Develop and maintain governed data models supporting Power BI reporting, including DAX measures, row-level security, and performance optimisation. • Support the transition from manual and duplicated reporting to automated, governed reporting solutions. • Prepare documented, validated datasets for use by EPMO AI tools and agents. • Apply AI-assisted engineering and automated anomaly detection to improve pipeline efficiency and data quality, ensuring all changes are tested before release. Requirements • 8–12 years of experience in data engineering, data architecture, or business intelligence development, with demonstrated ownership of production data pipelines. • Advanced SQL skills, including common table expressions, window functions, merge/upsert logic, and query optimisation. • Expert-level Power Query (M) and DAX skills, including the ability to write and optimise code independently. • Strong expertise in dimensional modelling, data warehousing, and semantic layer design. • Hands-on experience with ETL orchestration and pipeline engineering, including incremental loading, scheduling, monitoring, and failure recovery. • Experience with Microsoft Fabric, Azure Data Factory, or equivalent data engineering platforms. • Proven enterprise systems integration experience, preferably with SAP and Primavera P6. • Strong understanding of data governance, validation frameworks, metadata management, data lineage, and audit readiness. • Working knowledge of project controls within construction, infrastructure, or EPC environments, including earned value, cost and schedule reporting, and variance analysis. • Proficiency in Python for data manipulation and experience with version control practices. • Experience in construction, infrastructure, EPC, or large-scale project delivery reporting environments is highly preferred. • Gulf or wider MENA experience is an advantage, particularly within holding groups and subsidiary reporting structures. AI Fluency – Essential Demonstrated, practical use of AI as a core working tool is mandatory. Candidates must provide specific examples from their own professional experience, such as using AI to: • Build, test, document, or monitor data pipelines. • Identify data quality issues, anomalies, or inconsistencies more efficiently. • Improve data transformation, validation, and engineering workflows.