Senior Data & ML Engineer

Application Deadline: 15 March 2026

Department: Operations

Employment Type: Permanent

Location: South Africa

Reporting To: Data Engineering Manager

Description

For more than 20 years, Globeleq has been a long-term investor, developer, owner and operator of diversified power projects in Africa, where the company is one of the largest Independent Power Producers. With nearly 1,800MW of generation capacity in operation across 17 power plants in 7 countries, 485MW of new power projects in construction and >2,000MW in development, Globeleq is one of the largest independent power producers solely focused in Africa. Globeleq is 70% owned by British International Investment and 30% by Norfund, the development finance institutions of the UK and Norway, and has a proven track record for supporting the ongoing development of the African power sector.    

Globeleq’s various generation technologies include gas, wind, solar PV, battery energy storage (BESS), and geothermal. The company is also actively pursuing new opportunities which are emerging from the energy transition. In South Africa, Globeleq owns and operates renewable energy (RE) power plants throughout the country.


The Senior Data & ML Engineer is responsible for executing the technical implementation of Globeleq’s Data Transformation initiative on behalf of, and under the direction of the Data Engineering Manager. The role focuses on designing and building the data ingestion and processing platform, including automated data pipelines, an integrated Single Source of Truth (SSOT)database, cross-functional system integrations and AI/ML-ready data structures. 

The Senior Data & ML Engineer must translate strategic direction into concrete technical solutions, make sound architectural recommendations, and deliver scalable, robust, production-grade data capabilities that support reliable reporting, advanced analytics and machine learning use cases across the business. 

This role will form part of our technical shared services team, 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.

Key Responsibilities

1. Design, build and maintain end-to-end automated data pipelines from internal and external sources into a central data platform: 
   1. Develop an integrated and scalable Single Source of Truth (SSOT) database, consolidating data from ERP, OT/IoT, SharePoint and other platforms/systems and ensuring scalable data flows. 
   2. Develop modular and scalable data ingestion developing API integrations, SQL stored procedures and ETL frameworks, maintaining reliable, automated data ingestion between internal and external platforms to the SSOT database. 
   3. Own end to end data development solutions. Be a practical driver and enforcer of the Data Transformation plan on behalf of the Data Engineering Manager; escalating risks and developing solutions. 
2. Design and develop scalable data models (staging, core, marts, feature sets) that support strategic reporting, advanced analytics and ML. 
   1. Design scalable data models across clearly defined layers, including staging (raw landed data), core (cleaned and standardised single source of truth), marts (business-ready views for specific domains), and feature sets (model-ready tables for machine learning and advanced analytics). 
   2. Implement MLOps practices (versioning of data and models, CI/CD for models, monitoring, retraining strategies) as ML use cases mature. 
   3. Own end-to-end development and processing (data algorithms and ML solutions) 
   4. Ensure all data models, pipelines and storage approaches are AI/ML-ready, including feature-ready datasets for pattern recognition, prediction and anomaly detection 
3. Technical platform development, data orchestration and data management 
   1. Apply and enforce data management, security and governance standards in line with the Data Governance Policy (Data Engineering, Audit and Risk, Cyber Security and IT requirements). 
   2. Implement structured change management (version control, release processes, approvals, rollback plans). Maintain comprehensive technical documentation and change management records for architectures, pipelines, automations, environments and access. 
   3. Work with divisional data owners to reduce data silos, standardise data flows and ensure adherence to agreed standards and timelines. 
4. Asset Lifecycle Management & Platform Integration 
   1. Oversee the full onboarding and offboarding process for company assets and equipment within the central asset management platforms, ensuring accurate registration, configuration, and removal throughout the asset lifecycle.
   2. Ensure seamless data integration by validating that all asset information is correctly captured, synchronized, and aligned with the organization’s reporting frameworks and operational dashboards. 
   3. Maintain data integrity and platform compliance by routinely reviewing asset entries, resolving discrepancies, and coordinating with relevant teams to uphold consistent monitoring and reporting standards.

Skills and Competencies

1. Full-stack data engineering competence: 
   1. API integration (REST/JSON, auth, pagination, error handling) 
   2. ETL/ELT orchestration and job scheduling (Automated workflows) 
   3. Data modelling (staging, core, marts, feature sets); production operations (monitoring, alerting, incident response) 
   4. Strong SQL; proficiency in Python for data engineering and ML-enabling tasks; and solid programming foundations in Python, SQL and/or C# 
   5. Ability to make scalable architectural decisions and prepare data for ML and model integration into workflows.
   6. Solid ML foundations (feature engineering, evaluation, overfitting, drift) and ability to design data pipelines that are fit for ML. 
2. Senior, hands-on engineering mindset; comfortable owning technical direction.
   1. Proven ability to design and lead data platform or data product builds. 
   2. Self-directed and proactive: identifies problems, proposes solutions and drives implementation without detailed step-by-step direction. 
   3. Clear, structured communication skills. can explain technical options and trade-offs to non-technical stakeholders and leadership. 
3. Strong systems thinking and architecture skills: designs for scalability, maintainability and AI/ML-readiness from the outset.
   1. Strong engineering discipline: version control, testing, deployment processes, documentation and incident handling. 
   2. Enjoys building automation, integrations and ML-ready datasets. 
   3. Cross-team coordination and influence – able to work with multiple divisions, follow up with stakeholders and enforce agreed standards and timelines.

Experience, Knowledge and Qualifications

Minimum requirements: 

1. Degree in Computer Science, Information Systems, Engineering, Mathematics or a related field. Proficient in data engineering. 
2. 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).
3. Programming foundations in Python, SQL and/or C#, with clear evidence of making scalable architectural decisions. 
4. 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). 
5. Strong SQL skills (DDL/DML, performance tuning, stored procedures, views, functions). 
6. Proficiency in Python for data engineering and ML-enabling tasks, plus experience with ETL tooling and automation frameworks. 
7. 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: 

1. Hands-on experience with ML models in production (e.g. forecasting, classification, anomaly detection) and associated MLOps tooling. 
2. Exposure to neural networks/deep learning (e.g. TensorFlow, PyTorch) and modern ML pipelines.
3. 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.
4. Experience with data lakes/big data architectures and orchestration tools (e.g. Airflow, Prefect, Azure Data Factory or similar). 
5. Familiarity with AI/ML governance, model risk and secure data handling. 
6. Experience working with industrial/IoT or energy sector data.