Data Analytics Engineer - JHB
Confirmed on the employer's own hiring board on Oct 9, 2026. First seen by Alion on Oct 8, 2026. DVT scores A on the Alion truth index.
DVT is a leading technology consulting and software engineering company delivering innovative solutions across Africa and internationally. We partner with clients to solve complex business challenges through software engineering, cloud platforms, data, artificial intelligence, and digital transformation. Our teams combine deep technical expertise with a strong consulting mindset to create measurable business value.
Our Data Engineering and Analytics practice is at the heart of this work - building governed, reusable data products that enable self-service analytics at enterprise scale. We work with cutting-edge cloud platforms, including Microsoft Fabric, Databricks, and Azure Synapse, and we're proud of our culture of continuous learning, internal training, and technical excellence.
The Role
We are looking for a Data Analytics Engineer to join our team on a high-impact engagement.You will convert analyst-owned product specifications into governed, certified, and reusable data products on the Group data platform. You will own the semantic modelling layer that makes shared experiences measurable and comparable across channels and business units.
This is not a bespoke analytics delivery role. You will enable scale through reusable platform capability - building data products, analytical models, and semantic layers that allow Business Unit Analytics teams to self-serve trusted insight using consistent definitions, reusable patterns, and certified data foundations.
Requirements
Data Product Engineering
Design, build, and maintain governed data products on the Group data platform, including bronze, silver, and gold layers, reusable data marts, and certified analytical data products.
Analytical & Semantic Modelling
Define and maintain semantic models, star schemas, KPI logic, and behavioural entities such as events, sessions, journeys, flows, steps, intents, and transitions.
Maintain definition consistency across channels and business units.
Data Quality, Contracts & Conformance
Implement validation checks, testing frameworks, data contracts, governance controls, and certification evidence so that data product quality is demonstrated rather than assumed.
Insight Delivery & Platform Analytics
Build analytical products and Power BI assets that support descriptive, diagnostic, predictive, and prescriptive analytics over governed data products.
Enablement & Self-Service
Create reusable datasets, templates, onboarding material, usage guidance, and playbooks so that consuming analytics teams can use the data products without re-engineering the foundations.
AI-Enabled & Conversational Analytics
Structure datasets and semantic models so that AI agents, conversational analytics, and natural language querying return accurate outputs against governed business logic and definitions.
Automation, Engineering Practice & Operations
Use Python, SQL, Azure DevOps, Git, and CI/CD practices to automate analytics delivery, version control solutions, and support performance monitoring and optimisation of owned products.
Experience
5-10 years of relevant experience as a Data Engineer and Analyst.
Experience in Agile methodologies and ways of work.
Strong experience across data engineering, analytics, and data modelling, ideally in digital, channel, behavioural, platform, or financial services environments.
Experience in enterprise-scale or regulated environments is advantageous.
Behavioural Analytics technologies experience would be advantageous.
Strong product mindset with experience of having built or contributed to a framework, template, library, accelerator, standards, or shared semantic model.
Analytical & Technical Skills
Strong SQL and Python, including data transformation, analysis, automation, and reusable component development.
Working knowledge of Microsoft Fabric, Databricks, Delta, Spark / PySpark, Azure Data Factory, Synapse, ADLS Gen2, and event or streaming patterns.
Dimensional modelling and semantic modelling expertise, including Kimball star schema design, data marts, Power BI semantic models, and DAX.
Practical understanding of data governance, Microsoft Purview, Unity Catalog, data contracts, data quality, testing, lineage, and certification.
Azure DevOps, Git, CI/CD, and infrastructure as code exposure.
Qualifications
Bachelor's or Master's in Computer Science / Engineering / Business Analytics.
Microsoft DP-600 or DP-700 certification preferred; DP-203, PL-300, AZ-900, or relevant Databricks certifications recognised.
Minimum Requirements
Matric (Grade 12) certificate.
Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent practical experience).
At least one relevant professional certification (e.g., Microsoft DP-600, DP-700, DP-203, PL-300, AZ-900, or Databricks certifications).