Stellenbosch: Campaign Data Analyst (Hybrid)

Team: Decision Analytics Reports to: Decision Analytics Specialist Location: Stellenbosch / Cape Town (Hybrid) People Leader Role: No About the Organisation This organisation operates in the payments and financial services environment, supporting high-volume transactional activity across South Africa. The business is highly data-driven and operates in a regulated, performance-focused environment where decisions are guided by analytics, experimentation, and measurable outcomes. The culture values analytical rigour, clear thinking, and execution based on evidence rather than intuition. Purpose of the Role The Campaign Data Analyst is a pure data and analytics role responsible for analysing customer and transactional data, translating analytical outputs into operational decisions, and continuously optimising performance using data. This role sits firmly within the Decision Analytics function and is not a marketing or campaign management role. While analytical outputs may be deployed into customer communication channels, ownership of branding, messaging, or marketing strategy does not sit with this role. The successful candidate will come from a data analyst, business analyst, BI analyst, or data science-adjacent background, with strong hands-on capability in SQL, Power BI, and analytical problem-solving. Key Responsibilities Data analysis and decision support Analyse large transactional, behavioural, and customer datasets to identify patterns, risks, and opportunities. Translate analytical findings into clear, actionable recommendations that drive business decisions. Support decision-making related to customer behaviour, performance optimisation, and operational outcomes. Data extraction, validation, and modelling support Write and optimise SQL queries to extract, validate, and analyse datasets from multiple sources. Work with structured data models and support segmentation, scoring, or rule-based decision frameworks. Partner with data scientists and analytics specialists to operationalise analytical outputs. Performance monitoring and optimisation Build and maintain dashboards and reporting views using Power BI or similar tools. Monitor performance trends, identify anomalies, and investigate root causes using data. Track outcomes over time and recommend optimisation actions based on evidence. Experimentation and analytical testing Design and evaluate structured tests (e.g. A/B tests, rule changes, threshold adjustments). Define hypotheses, success metrics, and evaluation frameworks. Analyse test results and present findings in a clear, business-focused manner. Cross-functional analytics delivery Work closely with Product, Engineering, Risk, and Operations teams to ensure analytical logic is correctly implemented. Act as a bridge between analytics outputs and operational execution. Maintain clear documentation of data logic, assumptions, and analytical decisions. Governance, accuracy, and compliance Ensure analytical processes follow data governance, privacy, and regulatory requirements. Maintain audit-ready documentation of datasets, queries, logic, and outputs. Prioritise data accuracy, reproducibility, and defensibility in all analysis. Core Competencies Strong analytical and critical-thinking capability. High attention to detail and data integrity. Ability to work with ambiguity and complex datasets. Structured, methodical approach to problem-solving. Comfortable explaining data-driven insights to non-technical stakeholders. Skills & Technical Requirements Essential Strong SQL capability (writing, optimising, and validating queries). Proven experience with Power BI or equivalent BI tools. Solid understanding of data structures, metrics, and analytical frameworks. Advanced Excel or equivalent analytical tooling. Experience working in analytics, BI, or decision-support roles. Advantageous Python experience for data analysis or automation. Exposure to data science concepts such as clustering, scoring, or predictive modelling. Experience in fintech, payments, lending, or other regulated environments. Experience & Qualifications 3–5 years’ experience in a data analytics, business analytics, BI, or decision analytics role . Demonstrated ability to translate data into operational or commercial decisions. Degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative field preferred. Working Environment Hybrid working model with flexibility. Performance-driven, analytics-led culture. Strong emphasis on learning, accuracy, and continuous improvement.