Analytics Engineer
Analytics Engineer at wePlace in Cape Town, sitting between Data Engineering and Analytics: building reliable, performant data models, data marts, semantic layers and scalable pipelines, applying CI/CD, testing and orchestration (e.g. Airflow, Prefect) with strong data governance to power client engagement decisions.
- Compensation
- Not disclosed
- Location
- Cape Town, South Africa
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Data analytics
- Posted
Skills
Must have
- At least 5-8 years experience in analytics engineering or a closely related data role
- Bachelor's Degree in Analytical/Data/Technical or Other
- Transform complex data into accessible, reliable and performant data models and insights
- Collaborate with stakeholders and partner with Data Architects and Data Modellers to design scalable data models
- Design and build data models, data marts, semantic layers and scalable transformations and pipelines
- Apply CI/CD practices, testing frameworks and orchestration tools (e.g., Airflow)
- Embed data governance and quality assurance into every solution
- Advanced grasp of data modelling best practices
- Advanced grasp of data governance and quality assurance
- Advanced grasp of cloud data platforms and orchestration tools (e.g., Airflow, Prefect)
- Understanding of software engineering principles (e.g., CI/CD, testing)
- Clear criminal and credit record
Full description
About the team
The Insights and Media Solutions team drives client data analytics and client optimisation strategies made up of data and software engineering professionals alongside engagement strategy and operations specialists. Together, the team turns client data into meaningful, actionable intelligence that reaches millions of people. The Analytics Engineer sits at the heart of that bridging the gap between data engineering and analytics and building the data foundation that powers client engagement decisions.
Why this role exists
The Analytics Engineer II exists to bridge the gap between Data Engineers and Data Analysts transforming complex data into accessible, reliable and performant data models and insights that drive decision-making across the organisation. This is not just a modelling role it's an engineering role with an analytics lens. The team needs someone who can take complex, raw data and shape it into clean, trusted data assets that analysts and engineers can actually use, fast.
What you'll do
- Transform complex data into accessible, reliable and performant data models and insights that drive organisational decision-making
- Collaborate with stakeholders to understand analytical needs partnering with Data Architects and Data Modellers to design scalable data models aligned with enterprise strategy
- Design and build efficient, reproducible data assets including data models, data marts and semantic layers along with scalable transformations and pipelines that empower Analytics Engineers and Data Analysts to deliver insights and reports with speed, consistency and minimal friction
- Apply CI/CD practices, testing frameworks and orchestration tools (e.g., Airflow) to productionise and maintain robust data solutions
- Embed data governance and quality assurance into every solution from the ground up
Minimum Experience
- At least 5-8 years experience in analytics engineering or a closely related data role
- Transform complex data into accessible, reliable and performant data models and insights that drive organisational decision-making
- Collaborate with stakeholders to understand analytical needs partnering with Data Architects and Data Modellers to design scalable data models aligned with enterprise strategy
- Design and build efficient, reproducible data assets including data models, data marts and semantic layers along with scalable transformations and pipelines that empower Analytics Engineers and Data Analysts to deliver insights and reports with speed, consistency and minimal friction
- Apply CI/CD practices, testing frameworks and orchestration tools (e.g., Airflow) to productionise and maintain robust data solutions
- Embed data governance and quality assurance into every solution from the ground up