Required

• 3–5 years of experience in an analytics or BI role, with a track record of building self-service dashboards and automated reporting that reduce ad-hoc analyst dependency.
  
• Advanced SQL and data transformation expertise: dimensional modelling, ETL/ELT pipelines, and production-grade dataset design.
  
• Solid grounding in statistical methods: A/B test design, regression analysis, and the ability to distinguish correlation from causation in messy business data.
  
• Demonstrated ability to communicate analytical findings to non-technical stakeholders in writing and in person.
  

Product Analytics

• Design and maintain dashboards covering acquisition funnels, conversion rates, channel performance, and customer segmentation across online and retail.
  
• Analyse the impact of promotional campaigns, pricing changes, and device mix decisions on conversion, revenue, and customer lifetime value.
  
• Partner with commercial and marketing stakeholders to design experiments (A/B tests, holdout groups) and deliver attribution analysis that separates signal from noise.
  
• Identify behavioural patterns in the customer journey that inform product development, UX decisions, and go-to-market strategy.
  

Credit and Portfolio Analytics

• Build and maintain self-service dashboards for portfolio health, delinquency trends, collections performance, and risk segmentation.
  
• Conduct causal investigations correlating device performance, customer demographics, promotional history, and payment behaviour to identify true default drivers.
  
• Support underwriting optimisation through data-driven analysis of approval criteria, score thresholds, and policy change outcomes.
  
• Translate credit performance data into clear, actionable recommendations for the credit and risk leadership team.
  

BI Infrastructure and Data Quality

• Design and automate data pipelines that transform raw credit bureau data, payment histories, and operational systems into reliable, production-grade datasets.
  
• Partner with the Analytics Engineering Lead to establish data quality standards, documentation practices, and analytical frameworks that enable progressive data autonomy across teams.
  
• Reduce dependency on ad-hoc analyst requests by building repeatable, self-service data products.