Business Intelligence Analyst

Qualifications, Skills and Experience:

• Bachelor’s degree in data science, Statistics, Computer Science, Mathematics, Economics, Business Analytics, or related field
  
• Professional qualifications in Data Analytics (e.g., Microsoft Certified Data Analyst, Google Data Analytics, Tableau, Power BI, SAS, or equivalent)
  
• Membership in a Data Science or Analyst Association is an added advantage.
  

Work Experience:

• Minimum of 3 years of experience in data analytics, business intelligence, data reporting, or business data analysis within a commercial or financial services environment
  
• Experience leading or mentoring analytics teams or delivering cross-functional data projects is desirable
  
• Experience with BI and data visualisation tools (e.g., Power BI, Tableau), data querying languages (e.g., SQL), and data management platforms is highly desirable.”
  
• Knowledge of data governance, data privacy, and regulatory frameworks related to data management is an advantage.”
  
• Experience working in agile teams or on agile data projects is an added advantage.
  

Strategic Leadership:

• Develop and execute the business intelligence strategy aligned with the department’s goals and objectives.
  
• Provide leadership and serve an integral role across department units by defining value-centric initiatives driven by data in line with department and organisational goals.
  
• Provide thought leadership and analytical rigour on a range of business opportunities.
  
• Lead and develop analytical teams to effectively execute the department strategy.
  

KPI Automation & Tracking:

• Enhance monitoring and tracking of strategic departmental KPIs, including their automation to facilitate execution.
  

Data Translation:

• Work with partner data to segment and profile information, ensuring visibility of coverage status.
  
• Serve as a conduit between analytical teams and decision-makers, bridging the communication divide and adding value to insights to propel the department towards its goals.
  
• Utilise behavioural analytics to provide insights into lifestyle-based savings and offer guidance on opportunities and challenges.
  

Data Governance and Architecture:

• Implement the Fund’s data security, privacy, compliance, and governance policies, frameworks, quality standards, and management processes across the department’s data.
  
• Develop and maintain data models that support the department’s reporting and analytics needs.
  

Data Democratisation:

• Adopt an always-on approach to ensure the availability of data for decision-making across departmental units, enabling stakeholders to access the data they need anytime, anywhere.
  

Business Reporting and Analytics:

• Collaborate with stakeholders to define and prioritise key performance indicators (KPIs) and reporting requirements.
  
• Identify trends, patterns, and insights from data analysis to support strategic decision-making for the department’s initiatives.
  
• Oversee the creation and delivery of accurate, timely, and actionable reports, dashboards, data visualisations, and insights.
  

Cross-Functional Collaboration & Stakeholder Engagement:

• Lead cross-functional engagements with other units within and outside the department, partnering with stakeholders and implementing agile methodologies in project execution.
  
• Collaborate with IT teams to ensure data availability, integrity, and accessibility for business intelligence initiatives.
  
• Work closely with stakeholders to translate business requirements into data and analytics solutions.
  

Research & Development:

• Foster R&D initiatives to source best-in-class approaches, including the use of emerging technologies to effectively execute the department strategy.
  

Monitoring and Evaluation:

• Monitor and evaluate the effectiveness of business intelligence initiatives, identifying areas for improvement.
  
• Implement and optimise data analytics tools, technologies, and platforms to enhance data processing, analysis, and reporting capabilities.
  
• Drive a culture of data-driven decision-making across the department.