The Focus Group Sandton, South Africa 18 June 2026 Junior / Mid 1000 - 10001 Contract Cost to Company 3 years - 5 years Skills Extract Transform Load (ETL) SQL Data Modeling Data Azure Databricks Design Engineering Analytical Industries Banking Financial Services Job Description
Role Purpose
The Analytical Engineer is responsible for transforming complex, raw data into structured, governed, and analytics-ready datasets that enable advanced reporting, analytics, and decision-making across the organisation.
This role bridges data engineering and analytics by combining strong data modelling expertise with data transformation and analytical capabilities to support enterprise data products and business outcomes.
Key Responsibilities
Data Modelling & Design
Design and implement scalable data models (e.g., dimensional and Data Vault) to support analytical use cases.
Ensure alignment to enterprise data architecture and modelling standards.
Data Transformation & Engineering
Develop and maintain ETL/ELT processes to ingest, cleanse, and transform data from multiple sources into trusted datasets.
Deliver curated, analytics-ready data layers for consumption by downstream users.
Analytics Enablement
Enable business intelligence, reporting, and advanced analytics use cases through high-quality data provisioning.
Partner with data scientists and analysts to support modelling, insights, and data-driven decision-making.
Data Quality, Governance & Controls
Ensure data integrity, lineage, and consistency across datasets.
governance frameworks and standards to support auditability and regulatory compliance.
Stakeholder Collaboration
Engage with business and technical stakeholders to translate requirements into data solutions.
Support delivery within cross-functional squads aligned to enterprise data products.
Required Skills & Experience
Strong experience in data modelling (Data Vault, dimensional modelling)
Proficiency in SQL and Python for data transformation and analysis
Experience with modern data platforms (e.g., Azure, Databricks, Microsoft Fabric)
Solid understanding of data warehousing, ETL/ELT, and analytics pipelines
Familiarity with data governance, quality frameworks, and lineage
Proven ability to combine engineering discipline with analytical problem-solving
Key Competencies
Analytical thinking and problem-solving
Attention to data quality and detail
Strong stakeholder engagement and communication
Delivery focus within agile environments
Governance and compliance awareness
Deliverables / Success Measures
Delivery of trusted, analytics-ready datasets aligned to business requirements
Implementation of scalable and reusable data models
Adherence to enterprise data governance and quality standards
Enablement of timely and accurate reporting and analytics outcomes
Contribution to data product delivery within GDA squads
Nice-to-Have
Experience in banking or financial services data environments
Exposure to real-time/streaming data processing frameworks
Understanding of DataOps and CI/CD practices
Certification in data modelling or cloud platforms
