Maintenance engineer – data science projects
Hire Resolve · Gauteng, South Africa ·
- Category
- Data science
- Experience
- 2+ years
Hire Resolve · Gauteng, South Africa ·
A consulting company in Johannesburg is hiring a Maintenance Engineer to keep production data science systems — predictive models, data pipelines, dashboards, and APIs — reliable, monitored, and up to date. The role centers on Python, R, SQL, Git/Docker, CI/CD, and cloud platforms (AWS/GCP/Azure).
A leading consulting company and aforward-thinking team is looking for a Maintenance Engineer to join their team in Johannesburg, GP. Your main mission will be to ensure the continued reliability, performance, and evolution of advanced data science systems. You'll play a critical role in supporting the long-term value of deployed models, data pipelines, dashboards, and APIs - keeping them accurate, stable, and aligned to business needs. This position blends technical vigilance with continuous improvement and stakeholder collaboration.
Key Responsibilities Monitor the health and performance of production data science systems, including predictive models, dashboards, and data pipelines. Diagnose issues such as data drift, performance degradation, or infrastructure instability, and implement timely fixes. Automate monitoring tasks and health checks related to data quality, forecast accuracy, and pipeline execution. Update and patch environments and applications, ensuring smooth operation across versions and dependencies. Collaborate with engineers and data scientists to refactor and optimize code for long-term maintainability. Maintain detailed documentation and change logs to ensure knowledge sharing and traceability. Support incident response, including root cause analysis and post-incident improvements. Ensure compliance with all applicable data privacy, security, and regulatory standards. Requirements Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. Minimum 2 years' experience in a data engineering, MLOps, or system maintenance role. Solid understanding of data science production workflows, including pipelines and model lifecycle. Proficient in Python and R with strong debugging and refactoring capabilities. Confident in SQL and managing large-scale datasets in production. Experience with CI/CD, Git, and containerization tools like Docker. Familiarity with cloud infrastructure (AWS, GCP, or Azure) and Dev Ops best practices. Strong analytical, problem-solving, and communication skills.