Data Analyst X3

Purpose of the Job

• The Data Analyst is responsible for contributing to the development and integration of approved Management Information System MIS dashboards and reports through analysing and transforming complex data, in line with company rules and regulations.

Job Responsibilities Analytics Input and Support

• Contribute to the development, implementation, and continuous improvement of data and analytics-related processes, procedures and operational plans, in line with departmental and company goals.
• Provide input into data requirements, model-design specifications and needs to support enhancement of operational efficiency within the Analytics team.
• Stay abreast of and provide technical advice and guidance on innovative Business Intelligence BI tools, trends and new visualisation technologies in the market to promote ongoing innovation.

Data Analysis and Reporting

• Collaborate with the Senior Business Intelligence BI Specialist and Analytics teams to execute assigned projects by integrating advanced analytical outputs into reporting deliverables.
• Develop, maintain, and enhance Management Information Systems MIS reports, dashboards, and analytical outputs in collaboration with relevant stakeholders, ensuring achievement of project deliverables and objectives.
• Analyse datasets to identify trends, anomalies, and performance insights that support business decision-making.
• Review data to ensure integrity reliability is maintained across systems, escalating and proposing corrective action / solutions for review and obtain approval by the Senior BI Specialist before implementation of corrective actions.
• Support integration and monitor performance of approved reporting and analytical solution implementation.
• Identify and escalate any risks or concerns to the Senior Specialist for review, while proposing and obtaining approval of solutions for implementation.
• Prepare and present analytical findings and visual outputs in business-relevant format, to inform decision-making.
• Recommend improvements to enhance reporting efficiency, data presentation, and information accessibility.

Data Governance, Compliance and Quality Assurance

• Adhere to all data-governance and reporting standards in compliance with the Protection of Personal Information Act POPIA,
• Financial Sector Conduct Authority FSCA requirements, and internal company policies, rules, and standards.
• Perform data-quality checks, validations, and reconciliations to maintain accuracy of reports and datasets.
• Maintain documentation of report structures, datasets, and data transformations in relevant data repositories to promote data hygiene, integrity, and reproducibility.
• Identify data risks, concerns, integrity issues, or compliance concerns to the Senior Business Intelligence BI Specialist for review and resolution.
• Support continuous improvement initiatives related to data quality, integrity, and governance frameworks.

Qualifications and Experience

• Bachelor's degree in Computer Science, Information Technology, Data Science, or a relevant field NQF Level 7
• Advanced degree or MBA preferred Ideal
• 3+ years of experience as a Data Analyst
• Experience in the Banking Industry Advantageous

Skills and Attributes

• Uncompromising integrity, honesty, and professional ethics.
• Respectful and professional conduct at all times.
• Strong personal accountability and ownership.
• A commitment to excellence in every deliverable.
• High levels of resilience and perseverance when facing obstacles.
• Self-motivation and the ability to operate independently.
• Continuous learning and intellectual curiosity.
• Loyalty to team objectives and organisational success.

Phase Target, Skills & Algorithms, Tech Stack Mapping Core Data &BI Engineering

• Database optimization, automated ETL pipelines, stored procedures. SQL Server SSMS, SSIS, Power Query
• Advanced data modeling, context filtering, row-level security, cloud deployment. Power BI DAX, Tableau
• Statistical profiling, macro migration, legacy predictive validation. SAS

Predictive AI & Automation

• Binary classification, feature engineering for upsell/crosssell propensity modeling. Python scikit-learn, XGBoost, R, SQL Server
• Regression trees, ensemble learning, automated lead generation scoring engines. Python, SQL Server, Power Query Survival analysis, lifetime value LTV estimation, NLP for service ticket intent. Python lifelines, HuggingFace, R, Power BI

Advanced Optimization & Geo

• Location allocation algorithms, spatial clustering, spatial regression. ArcGIS, Python GeoPandas, PySAL
• Constrained optimization, attribution modeling, marketing mix modeling MMM. Python SciPy.optimize, R
• Bridging deep tech with executive business decisions, setting architectural patterns. Full Ecosystem Transformation