Job Description

Are you a data-driven problem solver eager to launch your career in analytics while working with one of South Africa's most iconic restaurant groups?



Join

Spur Corporation

as a

Data Analyst Graduate

and gain exposure to real-world business challenges where your skills in data analysis, modelling, and visualization will directly contribute to strategic decision-making and customer experiences.



As a Data Analyst Graduate at Spur Corporation, you'll work alongside experienced analysts, data scientists, and business teams to transform complex data into actionable insights. This is your opportunity to apply your academic knowledge, learn industry best practices, and develop into a well-rounded data professional.

KEY RESPONSIBILITIES & DUTIES:

Collaborate with IT and business teams to extract, clean, and integrate data from multiple sources for analysis. Assist in analysing customer behaviour, sales patterns, and operational data to uncover trends and opportunities. Apply statistical and analytical techniques to support business problem-solving. Contribute to building dashboards, reports, and data visualisations that help stakeholders make informed decisions. Support senior analysts in predictive modelling, segmentation, and forecasting projects. Participate in A/B testing and experimentation to measure the impact of business initiatives. Ensure data accuracy, integrity, and quality through validation and consistency checks. Stay curious and up to date with emerging data science tools, technologies, and practices.

REQUIREMENTS:

Bachelor's degree in data science, Statistics, Mathematics, Computer Science, or a related field (Honours or postgraduate qualification is advantageous). Strong foundation in data analysis, statistical methods, and problem-solving. Knowledge of SQL for querying and managing datasets (required). Experience (academic or project-based) with data visualisation tools such as Power BI, Tableau, or similar. Understanding of predictive modelling, machine learning basics, or segmentation techniques (academic exposure acceptable). High attention to detail with a focus on data quality and accuracy. * Eagerness to learn, collaborate, and grow in a fast-paced, cross-functional environment.