Our client, a leading bank based in Sandton, is seeking an experienced Analytical Engineer to join their enterprise data team on a 12-month contract. This role is ideal for a data professional who combines strong Data Engineering, Data Analysis, and Data Modelling expertise and enjoys transforming complex business requirements into trusted, analytics-ready data solutions.
The successful candidate will play a key role in enabling enterprise reporting, analytics, and data-driven decision-making by designing scalable data models, building robust data transformation pipelines, and ensuring data quality and governance standards are maintained.
Key Responsibilities
Data Modelling & Design
Design and implement scalable Data Vault 2.0 and Dimensional Data Models to support reporting and analytics requirements.
Ensure alignment with enterprise data architecture, modelling standards, and best practices.
Develop reusable and sustainable data structures that support business and regulatory requirements.
Data Engineering & Transformation
Develop and maintain ETL/ELT processes to ingest, cleanse, transform, and integrate data from multiple source systems.
Deliver curated, analytics-ready datasets for business intelligence, reporting, and advanced analytics.
Optimise data pipelines and data processing performance within enterprise data platforms.
Analytics Enablement
Partner with business stakeholders, analysts, and data scientists to deliver trusted data assets.
Perform data profiling, validation, and analysis to ensure accuracy and reliability.
Support data-driven decision-making through the provision of high-quality data products.
Data Governance & Quality
Ensure data integrity, consistency, lineage, and auditability across data assets.
Apply enterprise data governance frameworks and quality controls.
Support compliance with banking, regulatory, and governance requirements.
Stakeholder Engagement
Collaborate with business and technical stakeholders to understand requirements and translate them into effective data solutions.
Participate in Agile delivery teams and contribute to enterprise data product initiatives.
Bridge the gap between business requirements and technical implementation.
Minimum Requirements
Proven experience in Data Modelling, including:
Data Vault 2.0
Dimensional Modelling (Kimball Methodology)
Advanced SQL skills for data transformation, optimisation, and analysis.
Strong Python experience for data engineering and analytics.
Experience working with modern cloud data platforms such as
Azure
Databricks
Microsoft Fabric
Strong understanding of:
Enterprise Data Warehousing
ETL/ELT Processes
Data Pipelines
Analytics and Reporting Solutions
Experience with data governance, data quality frameworks, lineage, and metadata management.
Strong stakeholder engagement and communication skills.
Experience working within Agile delivery environments.
Preferred Experience
Banking or financial services industry experience.
Exposure to real-time or streaming data processing frameworks.
Knowledge of DataOps, DevOps, and CI/CD practices.
Relevant certifications in Data Modelling, Azure, Databricks, Microsoft Fabric, or Cloud Data Engineering.