Business Analytics Head

Role PurposeThe Business Analytics Section Head leads the transformation of enterprise data into predictive insights and actionable operational intelligence.

Moving beyond static reporting, this role drives diagnostic and prognostic modeling to optimize business efficiency, cost control, and strategic planning.

The Section Head bridges the gap between complex data engineering and executive strategy, enabling teams to make proactive, data-driven decisions.

Key Responsibilities

1.

Advanced Analytics & Insights EnablementDecision Support: Design and maintain predictive models that help business units generate accurate forecasts for cost control, procurement cycles, resource utilization, and Earned Value Management (EVM).Diagnostic & Trend Analysis: Implement data versioning and time-series models to analyze trend deviations and budget-vs-actual variances.Data Storytelling: Lead the design of dynamic, user-centric Power BI dashboards that provide diagnostic data journeys for business leaders rather than static charts.

2.

Analytics Architecture & Pipeline OversightUnified Data Layer: Oversee the architectural standards of a centralized cloud data warehouse, ensuring diverse operational data streams flow into a high-performance analytical layer.Data Transformation: Define and govern business logic, transformation rules, and reporting structures within end-to-end ETL pipelines.System Integration: Collaborate with IT teams to enforce robust data schemas, integration protocols, and REST API/JSON data exchange patterns.

3.

Data Governance & Quality ManagementGovernance Enforcement: Champion the Data Governance Framework, ensuring data ownership, classification, and lifecycle policies support high-integrity analysis.Master Data Standardization: Govern Master Data Management (MDM) standards across key corporate data domains (e.g., operational hierarchies, cost codes, vendor masters).Validation & Integrity: Implement automated validation checks and anomaly detection to guarantee data accuracy.Gap Analysis: Produce Data Gap Reports to identify analytical blind spots and coordinate with information systems teams to resolve them.

4.

Strategic Leadership & EngagementCross-Functional Partnering: Engage proactively with business unit heads (Finance, Procurement, HR, Operations) to uncover unmet analytical needs and deliver custom solutions.Roadmap & Strategy: Drive the section’s strategic roadmap, defining OKRs and KPIs aligned with the organization's digital transformation agenda.Tech Scouting: Monitor global trends in data science and analytics to introduce advanced capabilities like automated forecasting or machine-learning-driven risk tracking.

Education & CertificationsDegree: Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related quantitative field.Certifications: Professional certifications in advanced analytics or cloud data structures (e.g., Azure Data Engineer/Analyst, AWS Certified Data Analytics) are advantageous.Experience & SkillsOverall Experience: 8–12 years of progressive experience in data analytics, business intelligence, or data engineering, with at least 3 years leading analytical initiatives or teams.Technical Skills: Expert proficiency in Power BI (advanced DAX, Power Query, data modeling) and strong mastery of SQL for complex querying and transformation logic.Data Architecture: Solid understanding of system integration patterns, REST APIs, JSON/XML, and cloud data warehouse architectures.Soft Skills: Exceptional stakeholder management skills; ability to translate complex technical data into clear, commercial business insights for executive decision-making.Industry Note: Experience working within a construction, engineering, or real estate organization—including familiarity with project controls, WBS structures, and site operations data—is highly preferred.