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.

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