Data Engineer (MLOps / Analytics Focus)
Summary
Builds and maintains scalable Databricks pipelines, deploys ML models via MLflow, and creates Power BI dashboards to support analytics and reporting in a data-driven environment.
Johannesburg, South Africa | Posted on 05/21/2026
Our client is seeking a capable intermediate-level Data Engineer with strong MLOps and analytics experience to support the design, optimisation, governance, and monitoring of enterprise data and machine learning pipelines.
The successful candidate will play a critical role in ensuring scalable, sustainable, and efficient data processes while supporting analytics, ML model deployment, integrations, and reporting initiatives within a Databricks ecosystem.
This opportunity offers strong long-term potential, as contractors are typically retained for multi-year engagements.
Key Responsibilities
- Design, optimise, and maintain scalable data pipelines within Databricks.
- Ensure pipelines are efficient, sustainable, easy to debug, and user-friendly.
- Implement and maintain Delta Tables and Databricks notebooks.
- Perform data validation and basic data quality checks.
- Monitor and improve process governance and operational efficiency.
- Train, deploy, and monitor machine learning models using MLflow.
- Analyse model performance and business impact.
- Support model lifecycle management and deployment best practices.
Analytics & Reporting
- Develop Power BI dashboards and business insight reporting.
- Support data-driven decision-making through analytics solutions.
Integrations & Monitoring
- Monitor API data integrations and data sends.
- Troubleshoot integration failures and ensure data consistency.
Git-based Workflows
- Pull requests
- Branch syncing
- Collaborate with cross-functional teams including data scientists, analysts, and business stakeholders.
Qualifications
Degree or Diploma in:
- Computer Science
- Information Systems
- Mathematics
- Statistics
- or related field
Experience
- 3–5 years’ experience in Data Engineering or related roles.
- Hands-on experience with Databricks.
- Experience with MLflow and machine learning deployment processes.
- Experience with Power BI dashboard development.
- Strong experience with Git version control workflows.
- Exposure to API integrations and monitoring.
Technical Skills
- Databricks
- Databricks Notebooks
- MLflow
- Python
- SQL
- Power BI
- API Monitoring & Integration
- Data Quality & Governance
Advantageous Skills
- CI/CD for ML Pipelines
- Spark / PySpark
- Cloud-based data platforms
Soft Skills
- Strong analytical and problem-solving abilities
- Ability to work in collaborative environments