Description:
- A leading consulting company and a forward-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 DevOps best practices.
-
Strong analytical, problem-solving, and communication skills.
Contact Hire Resolve for your next career-changing move.
Our client is offering a highly competitive salary for this role based on experience.
Apply for this role today, contact Gaby Turner at gaby.turner@hireresolve.us or on LinkedIn
You can also visit the Hire Resolve website: hireresolve.us or email us your CV: itcareers@hireresolve.za.com
Requirements:
- A leading consulting company and a forward-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 DevOps best practices.
-
Strong analytical, problem-solving, and communication skills.
Contact Hire Resolve for your next career-changing move.
Our client is offering a highly competitive salary for this role based on experience.
Apply for this role today, contact Gaby Turner at gaby.turner@hireresolve.us or on LinkedIn
You can also visit the Hire Resolve website: hireresolve.us or email us your CV: itcareers@hireresolve.za.com
-
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.
-
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 DevOps best practices.
-
Strong analytical, problem-solving, and communication skills.