Director - Data/Analytics & Artificial Intelligence

Role Overview: Lead the company's enterprise data, analytics, and AI initiatives. This is a hands-on technical leadership role responsible for defining data and analytics strategy, partnering with business leaders, and building scalable analytics and AI solutions that drive business decisions. Key Responsibilities: Enterprise Data Leadership: - Lead the enterprise data strategy, including building a scalable cloud data platform and long-term analytics roadmap. - Define enterprise data models, KPIs, and governance frameworks to ensure quality, security, and data management best practices. - Manage role-based access control across all data sources. - Build and manage data ingestion pipelines from structured and unstructured data sources. AI & Analytics: - Develop AI-powered predictive models and dashboards to drive real-time decision-making and automate reporting. - Partner with leadership to define KPIs and deliver actionable insights through statistical analysis and machine learning. Technical Leadership: - Design and implement modern data architecture, including scalable pipelines that integrate disparate business systems. - Serve as a technical contributor for SQL, Python, and code reviews, while evaluating and introducing emerging AI tools. Business Partnership: - Serve as a trusted advisor to executive leadership, translating complex business challenges into actionable technical solutions. - Collaborate cross-functionally with business units to influence organisational priorities using data-driven insights. Team Leadership: - Build and mentor a high-performing, cross-functional data and analytics team. - Establish engineering standards and analytics best practices to foster a culture of innovation and continuous improvement. Qualifications: - 8+ years in data engineering, analytics, BI, or data science with technical leadership experience. - Strong Python experience for data engineering and analytics. - Familiarity with modern AI technologies including LLMs, vector databases, RAG, and AI agents. - Experience building enterprise data platforms and AI-enabled analytics. - Tech stack (GCP, BigQuery, Python, AI/LLMs, etc.)