Business Data Architect

Role overview

Data architecture and ETL

• Design dimensional models with fact tables, dimensions, and semantic layers; establish standards for naming, transformations, version control, and change management.
• Build automated ETL pipelines for SAP, Primavera P6, CPCO, DAMAS, and external submissions; implement schedules, incremental loads, orchestration, alerts, and recovery.
• Detect source schema changes and inconsistencies to prevent silent pipeline failures.

Schedule analytics and governance

• Transform Primavera P6 activity, WBS, resource, and cost data for schedule variance, float, critical path, progress curves, and forecast analysis.
• Reconcile source systems and reports; implement validation, quality checks, and exception reporting.
• Document end-to-end lineage so reported metrics can be traced from source through transformations to output.

Reporting and AI enablement

• Maintain governed Power BI models with DAX measures, row-level security, and performance optimisation; transition manual reporting to automated solutions.
• Prepare documented, validated datasets for EPMO AI tools and agents.
• Apply AI-assisted engineering and automated anomaly detection; test changes before release.

Experience and technical qualifications

• Require 8–12 years in data engineering, data architecture, or BI development, with ownership of production data pipelines.
• Require advanced SQL, expert Power Query (M) and DAX, and strong dimensional modelling, warehousing, and semantic layer expertise.
• Require hands-on ETL orchestration and pipeline engineering, including incremental loading, scheduling, monitoring, and recovery.
• Require experience with Microsoft Fabric, Azure Data Factory, or an equivalent platform; enterprise systems integration is required, preferably with SAP and Primavera P6.
• Require knowledge of data governance, validation, metadata, lineage, and audit readiness; working knowledge of project controls in construction, infrastructure, or EPC settings.
• Require Python for data manipulation and version control experience. Construction, infrastructure, EPC, or large-scale project reporting experience is highly preferred; Gulf or wider MENA experience is an advantage.

Essential AI fluency

• Practical use of AI as a core working tool is mandatory; candidates must provide specific professional examples.
• Examples include building, testing, documenting, or monitoring pipelines; identifying anomalies; and improving transformation, validation, or engineering workflows.