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.)