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. 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.

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. #J-18808-Ljbffr