Senior Data Engineer

Role Description
Develop financial and customerbehaviour models that guide the design forecasting and commercial management of the eBucks rewards programme The role combines quantitative analytics rewards economics and business judgement to balance customer value behaviour change and financial sustainability
Responsibilities And Skills
Build maintain and enhance customerlevel financial and behaviour models
Model rewardrule valueproposition and partnership impacts including scenarios sensitivities and forecasts
Support the annual Earn Rule Change lifecycle preanalysis modelling forecasting implementation support and testing
Analyse customer and transactional data and convert findings into clear commercial recommendations
Support budgeting cost management and customerlevel financial analysis
Apply model governance through validation peer review documentation and quality assurance
Automate repeatable work and develop reusable scalable analytical capabilities
Technical Skills
Strong SAS andor SQL Python or R advantageous
Advanced Excel and strong PowerPoint capability
Financial forecasting scenario modelling and customerbehaviour analytics
Ability to manipulate large datasets translate business rules into model logic and improve existing processes
Automation AIassisted analytics and visualisation experience advantageous
Qualifications Experience
Bachelors degree in Data Science Actuarial Science Statistics Econometrics Applied Mathematics Quantitative Finance Computer Science or a related quantitative field
Honours or postgraduate qualification advantageous
3-5 years experience in data science financial modelling actuarial quantitative or commercial analytics
Banking financial services loyalty or rewards experience advantageous
CORE COMPETENCIES
Quantitative problem solving and critical thinking
Commercial and financial acumen
Attention to detail and accountability for accuracy
Strong stakeholder engagement and clear communication
Proactive curious and continuousimprovement mindset
Ability to work through ambiguity and tight delivery timelines

Work arrangement
No