Key Responsibilities:System Backend Management: Define and enforce platform data schemas, and integration protocols across all managed systems. Data Engineering & ETL Process Management: Lead the design, development, and maintenance of enterprise data models tailored to construction operations covering project cost control, procurement, material tracking, subcontractor payments, progress measurement, and resource planning.Own end-to-end ETL (Extract, Transform, Load) pipeline development connecting ACC, SAP, Zoho, and other source systems into centralized data repositories.Establish data transformation rules and business logic that align with construction-specific workflows including earned value management (EVM), budget vs. actual cost reconciliation, WBS-level reporting, and procurement cycle tracking.Data Governance & Quality Management: Develop and enforce the organization's Data Governance Framework, covering data ownership, classification, quality standards, and lifecycle policiesDefine Master Data Management standards across key construction data domains: project hierarchy (WBS/SBS), cost codes, vendor/subcontractor master, material codes, and BOQ item structuresImplement data quality rules, automated validation checks, and anomaly detection processes to maintain data accuracy and consistency across all platforms.Publish and maintain the organization's Data Dictionary and Data Catalog, ensuring all datasets have clear definitions, ownership, and lineage documentation.Data Warehouse Architect and implement the organization's centralized data warehouse that integrates data from ACC, SAP, Zoho, and other operational systems into a unified analytical layer.Select and govern the appropriate data warehouse technology stack (cloud DWH) and define architectural standards for scalability, security, and performance.Implement data versioning, historical snapshots, and time-series structures required for construction project baselining, trend analysis, and forecasting.Information Management: Produce comprehensive Data Gap Reports that document current-state data availability, quality deficiencies, and process gaps and coordinate with Information Systems to evaluate suitable solutions (system enhancements or new tool procurement)Support the development of a centralized information architecture that aligns document management, BIM data, field data, and financial data into a coherent information ecosystem.Coordinate with project controls, engineering, and site teams to ensure data captured in field operations is structured, validated, and flows accurately into analytical systemsStrategic Planning & Stakeholder Engagement Develop and drive the BI & Data Management section's quarterly and annual strategic roadmap, defining OKRs and KPIs aligned to the Digital Construction department's transformation agenda.Prepare executive-level reporting and QBR presentations covering data platform health, initiative progress, data quality metrics, system adoption rates, and business value delivered.Engage proactively with business unit heads (Finance, Procurement, Project Controls, Engineering, HR) to identify unmet data needs and translate them into actionable BI and data solutions.Participate in digital transformation steering committees and represent the BI function in enterprise architecture decisions.Monitor industry trends in construction technology, data engineering, and BI tools; provide recommendations for new capabilities that enhance operational intelligence. Bachelor's degree in Computer Science, Information Technology, Engineering, or a related technical field.Professional certifications in data and analytics (e.g., Azure Data Engineer, AWS Data Analytics, etc..) advantageous.8 – 12 years in IT, data, or BI-related roles3+ years working within a construction or real estate organizationProficiency in SQL (T-SQL, PL/SQL, or equivalent) for complex querying, data transformation, and performance optimization.BI tool expertise: Power BI (DAX, Power Query, data modeling)Experience with REST APIs, JSON/XML data exchange, and system integration patternsSolid understanding of data governance frameworks