Data & AI Platform Section Head

Role Purpose

Strategy and Platform Operations

• Translate enterprise data and AI strategies into engineering roadmaps and platform enablement plans.
• Ensure platforms support analytics, AI, and enterprise data needs.
• Lead engineering, deployment, and operational support for data and AI platforms.
• Oversee provisioning, monitoring, and optimization for business-critical analytics and AI workloads.
• Ensure enterprise standards for security, resilience, performance, and reliability are met.

AI and Analytics Enablement

• Enable advanced analytics, AI, and reporting through high-quality, reliable, and accessible data.
• Support business teams and data scientists with platforms, tools, and environments for AI model development.
• Partner with analytics teams to optimize platform usage and measure adoption effectiveness.

Integration and Data Flow

• Ensure seamless data integration across applications, platforms, and enterprise systems.
• Oversee API and middleware support for data ingestion, transformation, and distribution.
• Maintain data availability, integrity, and flow across the data and AI landscape.

Reporting and Improvement

• Provide updates on roadmap execution, risks, and modernization priorities.
• Apply enterprise standards and best practices for platform engineering, monitoring, and maintenance.
• Maintain documentation for configurations, integrations, and operations to support compliance and audit readiness.
• Capture lessons learned, embed automation, and continuously improve platform maturity.

Policies and Processes

• Monitor day-to-day activities for compliance with stipulated policies and procedures.
• Identify opportunities to improve systems and processes, reduce costs, and increase productivity.

People Management

• Participate in on-the-job training, mentoring, and coaching of subordinates.
• Provide direction, prioritize tasks, delegate responsibilities, and monitor workflow.
• Promote a high-performance working environment aligned with organizational values.

Qualifications and Experience

• At least six years of experience in a related field or equivalent is required.
• A bachelor’s degree in engineering, computer science, information technology, or equivalent is required.
• Preferred certifications include CDMP, TOGAF, Agile certifications, cloud platform certifications, or Databricks Data Engineer.