Senior Analytical Cube Developer

We are seeking an experienced and strategic Senior Analytical Cube Developer to lead the design, development, and implementation of advanced analytical cube solutions in alignment with company standards and strategic goals.

Job Responsibilities

Strategic Input and Resource Planning

• Provide strategic input into cube development and analytics policies, procedures, and strategies in line with departmental and company objectives.
• Collaborate with the Analytics team to drive continuous improvement initiatives.
• Contribute to infrastructure, resource, and budgetary planning to support analytical cube projects and data engineering initiatives.

Solutions Development and Delivery

• Partner with business units and analytics team members to translate data engineering and cube development requirements into clear project objectives.
• Manage the full lifecycle and implementation of advanced analytical cube development and data engineering projects.
• Lead project resources, delegate tasks, and provide guidance to ensure delivery within budget and timelines.
• Ensure the seamless integration of metadata cube structures, tabular models, and related frameworks across operational business units.
• Track, report, and proactively manage project progress, risks, and potential delays.
• Monitor post-implementation solution performance to ensure long-term operational success and stability.

Data Governance, Compliance, and Quality Assurance

• Contribute to data management practices to ensure data quality, integrity, and accessibility across all analytics systems.
• Ensure compliance with the Protection of Personal Information Act (POPIA), Financial Sector Conduct Authority (FSCA) requirements, and internal data governance policies.
• Safeguard metadata, data quality frameworks, and data dictionaries while overseeing secure storage in designated repositories for knowledge retention and reproducibility.
• Align development practices with enterprise data governance standards and metadata requirements.
• Identify and resolve data integrity issues affecting cube outputs with prompt corrective actions.

People Management

• Set clear role expectations, priorities, and performance standards for direct reports.
• Lead the performance management cycle by setting goals, conducting regular reviews, and delivering timely feedback.
• Coach and mentor team members, support individual development plans, and coordinate relevant training opportunities.
• Manage team capacity, workflow allocation, leave, and workforce planning in partnership with HR.
• Foster a positive, inclusive, and collaborative team culture while ensuring adherence to HR policies and South African Labour Legislation.
• Address performance, conduct, and attendance matters promptly and professionally.

Qualifications and Experience

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field (NQF Level 7).
• Honours or Master's degree in a relevant field (NQF Level 8 / 9) is ideal.
• Minimum of 5 years of relevant advanced analytical cube development experience at a specialist level.
• Minimum of 3 years of managerial or team leadership experience.
• Relevant experience within the banking industry will be a distinct advantage.

Skills and Attributes

• Uncompromising integrity, professional ethics, and personal accountability.
• Respectful conduct, resilience, and perseverance when facing obstacles.
• Self-motivation with the ability to operate independently and commit to excellence.
• A strong commitment to continuous learning, intellectual curiosity, and organizational success.

Technical Stack and Phase Targets

Phase 1: Core Data & BI Engineering

• Skills & Algorithms: Database optimization, automated ETL pipelines, stored procedures, advanced data modeling, context filtering, row-level security, statistical profiling, and macro migration.
• Tech Stack: SQL Server (SSMS, SSIS), Power Query, Power BI (DAX), Tableau, and SAS.

Phase 2: Predictive AI & Automation

• Skills & Algorithms: Binary classification, feature engineering for upsell/cross-sell propensity modeling, regression trees, ensemble learning, lead generation scoring, survival analysis, lifetime value (LTV) estimation, and NLP for service ticket intent.
• Tech Stack: Python (scikit-learn, XGBoost, lifelines, HuggingFace), R, SQL Server, and Power Query.

Phase 3: Advanced Optimization & Geo

• Skills & Algorithms: Location allocation algorithms, spatial clustering, spatial regression, constrained optimization, attribution modeling, and marketing mix modeling (MMM).
• Tech Stack: ArcGIS, Python (GeoPandas, PySAL, SciPy.optimize), and R.
• Full Ecosystem Transformation: Bridging deep technology with executive business decisions and establishing architectural patterns.

How to Apply

If you meet the requirements and wish to apply, please forward your Curriculum Vitae (CV) to the designated recruitment channel.