D&T Head Of Data, BI & Analytics

The Head of Data Sciences, Analytics, AI, and BI is a senior strategic and operational leader within Aramexs Digital & Technology organization. This role spans thought leadership, strategic planning, capability building and operational excellence across the BI, analytics and AI landscape.

This role balances innovation, project delivery and steady-state operations, ensuring the end-to-end reliability, scalability, and operational performance of Aramexs analytics platforms

1. Strategic Leadership & Thought Leadership

• Develop and communicate a long-term vision and strategy for Data Sciences, Analytics, AI, and BI, aligned with Aramexs business strategy and growth objectives.
• Drive innovation in analytics and AI, exploring emerging technologies, algorithms and methodologies to enhance business decision-making and operational efficiency.

2. Customer & Business Engagement

• Proactively engage business stakeholders to understand complex requirements, desired outcomes, and strategic priorities.
• Design and deliver fit-for-purpose analytics and AI solutions that optimize operations, improve customer satisfaction, and drive revenue.
• Use insights from customer feedback, usage analytics, and emerging trends to identify continuous improvement opportunities.

3. Operational Excellence & Service Delivery

• Own end to end operations of enterprise analytics platforms, including GCP-based ecosystems (BigQuery, Dataflow, Pub/Sub, Looker) and legacy BI systems (SQL Server, Oracle BI, ETL pipelines).
• Ensure platform stability, performance, security and compliance through robust ITIL-aligned processes (incident, problem, change, release management).
• Define and maintain SLAs, SLOs, and operational KPIs for uptime, pipeline reliability, cost efficiency, and user satisfaction.
• Lead post-project operationalization, including service acceptance, monitoring, and continuous improvement loops.

4. Team Leadership and Capability Development

• Lead and mentor a high performing team of data scientists, AI engineers, BI analysts, and data operations engineers.
• Build organizational capability in analytics, AI, and data engineering, fostering a culture of innovation, accountability, and continuous learning.
• Establish team KPIs for operational performance, service quality, and business impact.

5. Demand Management and Project Oversight

• Oversee demand intake, prioritization and resource allocation for analytics and AI projects and BAU operations.
• Ensure successful Build to Run transitions, including operational readiness reviews, documentation, monitoring, access controls, and training.
• Balance project delivery with steady-state operations, ensuring responsiveness to unplanned business demands while maintaining service stability.
• Partner with Program and Platform leadership to drive operational readiness, post go-live support, and continuous improvement.

6. Financial and Vendor Management

• Develop and manage functional budgets aligned with corporate IT and finance guidelines.
• Identify revenue generating opportunities through data monetization or analytics-as-a-service offerings.
• Optimize resource allocation and operational costs to maximize ROI on analytics and AI initiatives.
• Manage strategic relationships with vendors and managed service providers, ensuring SLA compliance, cost-effectiveness, and alignment with business priorities.