Analytics Engineer

SUMMARY:
Transforming complex, raw data into structured, governed, and analytics-ready datasets

POSITION INFO:
The Analytical Engineer will be responsible for transforming complex, raw data into structured, governed, and analytics-ready datasets that enable reporting, business intelligence, advanced analytics, and data-driven decision-making across the organisation. This role bridges the gap between data engineering and analytics , combining strong data modelling , data transformation , data quality , and analytical problem-solving capabilities. The successful candidate will support enterprise data products by designing scalable data models, building trusted data layers, and ensuring that data is accurate, traceable, reusable, and aligned to enterprise governance standards. Key Responsibilities Data Modelling and Design Design, develop, and maintain scalable analytical data models to support business reporting, analytics, and data product requirements. Apply appropriate modelling techniques, including dimensional modelling and Data Vault , depending on the use case and enterprise standards. Translate business requirements into logical and physical data models. Ensure all models align with Nedbank's enterprise data architecture, naming standards, governance principles, and modelling frameworks. Build reusable and scalable data structures that support both current and future analytical use cases. Data Transformation and Engineering Develop, maintain, and optimise ETL\/ELT pipelines to ingest, cleanse, transform, and curate data from multiple source systems. Use SQL and Python to transform raw data into trusted and analytics-ready datasets. Build curated data layers that can be consumed by BI developers, analysts, data scientists, and business users. Support data warehouse and modern cloud data platform development. Ensure data pipelines are reliable, performant, maintainable, and aligned to engineering best practice. Analytics Enablement Enable reporting, dashboarding, business intelligence, and advanced analytics through the provision of high-quality datasets. Partner with analysts, data scientists, data engineers, product owners, and business stakeholders to understand analytical requirements. Support the creation of enterprise data products that deliver measurable business value. Help ensure that downstream consumers have access to consistent, well-structured, and trusted data. Provide technical and analytical input into how data should be shaped, interpreted, and consumed. Data Quality, Governance and Controls Ensure data integrity, consistency, lineage, and traceability across analytical datasets. Apply data quality checks, reconciliation controls, and validation rules. Support compliance, auditability, and regulatory requirements through governed data practices. Document data flows, transformation logic, business rules, lineage, and definitions. Identify and resolve data quality issues in collaboration with source system owners and data stakeholders. Stakeholder Collaboration Engage with business and technical stakeholders to understand requirements and translate them into practical data solutions.