Data Science / BI Lead

The BI Tech Lead is responsible for leading the design, development, and evolution of the organisation's data and analytics capability. This role will establish a scalable data foundation, drive the delivery of insights and predictive analytics, and enable both internal reporting and client-facing intelligence through digital platforms.

The role combines technical leadership, data architecture, and business insight generation, ensuring that data is transformed into a strategic asset.

• Design and implement a scalable data warehouse architecture
• Define and manage data structures, data models, and data governance standards
• Oversee data collection, integration, and transformation across multiple sources
• Ensure data quality, consistency, and reliability

• Lead the development of predictive models, analytics, and insight generation
• Identify opportunities to leverage data for decision-making and optimisation
• Translate business requirements into analytical frameworks and outputs
• Ensure effective use of data across operational and strategic functions

• Own and manage the organisation's Power BI environment
• Design and deliver interactive dashboards and management reports
• Replace and modernise legacy Excel-based reporting
• Improve accessibility and usability of data across the business

• Support transition to cloud-based data infrastructure
• Implement and manage API / OData-based data integration
• Develop scalable data pipelines to support reporting and analytics needs

• Collaborate with product teams to deliver data-driven insights within digital platforms
• Enable visibility, analytics, and reporting capabilities for clients
• Support the development of features that provide value-added insights to customers

• Lead and manage a team consisting of:
  • Data Scientists (Junior & Intermediate)
  • Data Analyst
• Provide technical guidance, mentorship, and development support
• Establish best practices, standards, and ways of working
• Drive a culture of innovation, curiosity, and continuous improvement

• Implementation of a robust and scalable data warehouse
• Delivery of accurate, timely, and actionable business intelligence
• Development of predictive models and analytical outputs
• Modernisation of reporting (transition from Excel to Power BI and cloud-based solutions)
• Establishment of efficient data pipelines and integration frameworks
• Delivery of client-facing insights and analytics capabilities
• High-performing, aligned, and continuously improving data team

• Bachelor's degree in:
  • Data Science
  • Computer Science
  • Information Systems
  • Engineering or related field
• Relevant certifications (advantageous):
  • Microsoft Certified: Data Analyst / Azure Data Engineer
  • Cloud certifications (Azure, AWS, or similar)

• 5+ years' experience in:
  • Business Intelligence / Data Engineering / Data Analytics
• Proven experience in:
  • Designing and implementing data warehouses
  • Developing data models and analytics solutions
  • Working with BI tools (Power BI preferred)
• Experience leading or mentoring a team
• Experience working in Agile or product-driven environments (advantageous)
• Exposure to logistics, supply chain, or operational environments (advantageous)

• Strong SQL and database management skills
• Experience with Power BI (dashboarding, data modelling, DAX)
• Data warehousing and ETL/ELT processes
• Understanding of cloud data platforms (Azure, AWS, or similar)
• Experience with API / OData integrations
• Knowledge of data science tools and techniques (Python, R, or similar)
• Data modelling, data structures, and pipeline design

• Strong analytical and problem-solving ability
• Ability to translate complex data into clear business insights
• Strong communication skills across technical and non-technical stakeholders
• Leadership and team development capability
• High attention to detail and quality
• Innovative mindset with a focus on challenging the status quo
• Ability to work in a fast-paced, evolving environment
• Strategic thinking with a hands-on approach