Senior Data Engineer, ML

Globeleq

Senior Data & ML Engineer executing technical implementation of data transformation at Globeleq. Designing and building the data ingestion platform for advanced analytics and ML use cases.

Posted 5/18/2026full-time🇿🇦 South AfricaSeniorWebsite

Tech Stack

Tools & technologies

ETLPythonSQL

About the role

Key responsibilities & impact

Executing the technical implementation of Globeleq’s Data Transformation initiative
Designing and building the data ingestion and processing platform
Delivering scalable, robust, production-grade data capabilities that support reliable reporting, advanced analytics and machine learning use cases
Contributing to the development of digital management systems, O&M projects implementations, integration of new power plants, and ongoing development within Globeleq’s Data Transformation Project

Requirements

What you’ll need

Degree in Computer Science, Information Systems, Engineering, Mathematics or a related field
5+ years in data engineering/data platform development at a senior/lead level (3+ years may be considered only with clear evidence of lead responsibilities in development)
Programming foundations in Python, SQL and/or C#
Hands-on responsibility for API integration (REST/JSON, auth, pagination, error handling)
ETL/ELT orchestration and job scheduling
Strong SQL skills (DDL/DML, performance tuning, stored procedures, views, functions)
Proficiency in Python for data engineering and ML-enabling tasks
Strong foundations in ML concepts

Benefits

Comp & perks

**Minimum requirements:**
- Degree in Computer Science, Information Systems, Engineering, Mathematics or a related field. Proficient in data engineering.
- 5+ years in data engineering/data platform development at a senior/lead level (3+ years may be considered only with clear evidence of lead responsibilities in development).
- Programming foundations in Python, SQL and/or C#, with clear evidence of making scalable architectural decisions.
- Hands-on responsibility for API integration (REST/JSON, auth, pagination, error handling); ETL/ELT orchestration and job scheduling; data modelling (staging, core, marts/feature sets); and production operations (monitoring, alerting, incident response).
- Strong SQL skills (DDL/DML, performance tuning, stored procedures, views, functions).
- Proficiency in Python for data engineering and ML-enabling tasks, plus experience with ETL tooling and automation frameworks.
- Strong foundations in ML concepts and practical experience preparing data for ML and integrating models into data workflows (even if not a pure data scientist).
**Advantageous:**
- Hands-on experience with ML models in production (e.g. forecasting, classification, anomaly detection) and associated MLOps tooling.
- Exposure to neural networks/deep learning (e.g. TensorFlow, PyTorch) and modern ML pipelines.
- Design and support workflow automation and lightweight data applications using tools such as Power Apps and Power Automate, integrating these solutions with the core data platform to enable efficient business processes.
- Experience with data lakes/big data architectures and orchestration tools (e.g. Airflow, Prefect, Azure Data Factory or similar).
- Familiarity with AI/ML governance, model risk and secure data handling.
- Experience working with industrial/IoT or energy sector data.

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Hard Skills & Tools

PythonSQLC#API integrationETLELTjob schedulingperformance tuningstored proceduresmachine learning