POSITION: BI Tech Lead - (Hybrid)
LOCATION: Bellville
REPORTING TO: Head of Tech Innovation

1. Role Purpose

• The BI Tech Lead is responsible for leading the design, development, and evolution of the organization’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.

2. Key Responsibilities
2.1 Data Architecture & Engineering

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

2.2 Data Analytics & Data Science

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

 
2.3 Business Intelligence & Reporting

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

 
2.4 Cloud & Data Integration

• 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.

 
2.5 Client-Facing Data & Platform Integration

• 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.

 
2.6 Team Leadership & Management

• 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.

3. Key Deliverables

• Implementation of a robust and scalable data warehouse.
• Delivery of accurate, timely, and actionable business intelligence.
• Development of predictive models and analytical outputs.
• Modernization 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 performance, aligned, and continuously improving data team.

 
4. Qualifications

• 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. Experience

• 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).

 
6. Technical Skills

• 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.

 
7. Soft Skills / Competencies

• 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.

 
8. Success Measures

• Adoption and effectiveness of BI and reporting tools.
• Quality and impact of insights delivered to the business.
• Efficiency and scalability of data architecture.
• Reduction in manual / legacy reporting processes.
• Delivery of data-driven features within digital platforms.
• Team performance, growth, and alignment.