Performance Analyst

Application Deadline: 15 March 2026

Department: Operations

Employment Type: Permanent

Location: South Africa

Reporting To: Performance Analytics 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 Performance Analyst is responsible for analysing operational and performance data across Globeleq’s renewable-energy assets to identify trends, underperformance and opportunities for operational efficiency. The role focuses on asset performance, alarms and loss drivers, using statistical and data-science techniques on large datasets to generate actionable insights for the site teams. The analyst works in close collaboration with the Data Engineering / Development team, who own data flows, architecture and platforms, and with the BI team, who focus on maintenance and materials-management reporting, to ensure that asset-performance questions are answered consistently and accurately.

Key Responsibilities

1.  Fleet performance monitoring and analysis:
   1.  Monitor asset performance using SCADA/DCS, historian, weather, production and maintenance data; highlight underperformance and issues business and site teams.
   2. Analyse availability, PR/PRstc, DC/string availability, alarms and fault codes at plant, subsystem and component level.
   3. Work with large datasets to identify trends, patterns and anomalies and translate these into clear performance insights and improvement/operational efficiency opportunities.
   4. Interrogate and validate data quality (e.g. gaps, anomalies, comms losses, configuration changes) 
2. Metrics, KPIs, measurement and verification (M&V):
   1.  Design, refine and document performance metrics, thresholds and losses(e.g. availability, PR, soiling, shading, grid/curtailment, equipment-related losses).
   2. Apply M&V approaches to quantify the impact of events, operational changes and projects on energy and revenue.
   3. Maintain visibility of key KPIs such as revenue losses, soiling, data integrity, DC availability and forecasting accuracy, and ensure these are consistently reported. 
3. Site performance studies and improvement initiatives
   1.  Manage structured data requests from the asset platform to Engineering and site teams related to plant performance and losses.
   2. Deliver performance insights using historical trend analysis (soiling-impact, degradation, long-term yield trends, tilt and alignment) to assess asset health, current and historic; and further provide operational efficiency projects. 
   3. Analyse alarm frequency and recurring equipment failures and link these to maintenance activities and reliability.
   4. Separate weather-driven effects from technical or operational causes of loss when assessing underperformance.  
4. Advanced analytics, algorithms and automation:
   1. Apply statistical and data-science techniques (e.g. time-series analysis, regression, clustering, anomaly detection, forecasting) to understand energy profiles, drivers and losses.
   2. Develop analytical workflows and algorithms in Python or R (e.g. performance baselines, anomaly scores, loss-breakdown models, early-warning indicators).
   3. Prepare analysis- and ML-ready datasets and collaborate with the Data & ML Engineering team on use cases such as fault prediction, production forecasting and automated anomaly detection.  
5. Reporting, dashboards and monitoring screens:
   1.  Monitor, develop and improve performance dashboards and monitoring screens (e.g. Pi Vision, historian views) to support efficient monitoring, event identification and strategic oversight.
   2. Produce regular and ad-hoc performance reports, clearly explaining drivers of underperformance, quantified impacts and recommended actions. 
6.  Root-cause analysis, projects and collaboration:
   1. Support and conduct root-cause-analysis (RCA) investigations using performance data, alarm histories and maintenance records.
   2. Participate in cross-functional projects and maintenance activities where performance analytics is required (e.g. engineering-support projects, CAPEX justification and post-implementation reviews).
   3. Assist with IoT-integration mapping by defining and validating tag mappings, naming conventions and required signals for performance analytics; implementation of data flows remains with the Development team.
   4. Provide performance analytics and loss quantification to support compliance and external reporting (e.g. IPP/NERSA reports, CPA notices, curtailment claims). 
7. Documentation and standards:
   1.  Document analytical methods, assumptions, algorithms, monitoring rules and calculation logic to ensure transparency, repeatability and alignment with data-governance requirements.
   2. Contribute to standardised performance methodologies, definitions, dashboards and analysis templates within the Performance Analytics Division.

Skills and Competencies

1. Statistical, analytical and data-science skills
   1.  Strong foundations in statistics and data analysis (e.g. distributions, regression, correlation, hypothesis testing, time-series analysis, basic forecasting and anomaly detection).
   2. Ability to design and test hypotheses, quantify uncertainty and interpret results in an operational context.
   3. Proven competency in troubleshooting and analysing data, including interpreting process charts, schematics and single-line diagrams.
   4. Ability to handle large datasets and extract clear, concise performance insights.
2.  Programming, data handling and tools
   1. Practical experience in Python or R for data wrangling, analysis, visualisation and building custom analytical models or algorithms.
   2. Experience working with time-series datasets and transforming them into analysis-ready structures.
   3. Competence with data-visualisation and model development (dashboarding tools such as Power BI, MatplotLib etc.)
3.  Domain knowledge and operational understanding
   1.  Understanding of renewable-energy asset-performance concepts (e.g. availability, PR/PRstc, yield, curtailment, grid constraints, alarms, maintenance events).
   2. Familiarity with SCADA/DCS and industrial-data concepts (tags, signals, timestamps, statuses) and, ideally, PI/AVEVA or similar historians.
   3. Understanding of maintenance and asset-management data (work orders, equipment hierarchies, CMMS/ERP such as IFS) and how these interact with performance analytics; maintenance execution and materials management remain the responsibility of Engineering, O&M and BI teams.
4. Mindset and behaviours
   1.  Strong analytical curiosity and healthy scepticism; questions patterns, definitions and assumptions and seeks to understand the drivers behind the data.
   2. High learning proficiency and interest in data science, performance analytics and renewable-energy operations.
   3. Able to manage multiple projects, prioritise work and maintain quality under limited supervision; demonstrates ownership of assigned analyses and deliverables.
   4. Able to manage stress and high-pressure situations, particularly during events, outages and urgent investigations.

Experience, Knowledge and Qualifications

Minimum requirements: 

1. Degree in Engineering (Electrical / Mechanical / Industrial), Data Science, Statistics, Mathematics, Physics or a closely related technical field.
2.  1–3 years’ experience in an asset-performance, energy-analytics, industrial-analytics or data-science role working with operational time-series data (not purely financial or commercial analytics).
3.  Hands-on experience analysing SCADA/DCS, historian or IoT / time-series data in a production or operations environment.
4. Demonstrated proficiency in SQL for data extraction, joining and aggregation from relational databases / data warehouses.
5.  Practical experience in Python or R for data wrangling, statistical analysis, visualisation and development of custom analytical models or algorithms.
6.  Experience building and maintaining performance reports or dashboards (e.g. Power BI, MatplotLib or similar) used by operational stakeholders.
7.  Practical application of statistical and/or data-science techniques to real-world datasets (e.g. regression, time-series modelling, anomaly detection, segmentation, forecasting).
8.  Experience working with cross-functional teams (operations, engineering, maintenance, data/IT) and turning analytical findings into concrete actions or projects.  

Advantageous: 

1. Familiarity with renewable-energy assets (solar PV, wind and/or storage) and their key performance KPIs (availability, PR, yield, curtailment, grid events, alarms). 
2. Direct experience in solar PV, wind or battery energy-storage performance analysis with site-level or fleet-level responsibility.
3.  Exposure to advanced analytics / ML for operational use cases (e.g. automated anomaly detection, fault prediction, production forecasting, condition monitoring).
4.  Experience with time-series historians (e.g. AVEVA PI, TGS Prediktor or similar industrial data platforms).
5.  Familiarity with CMMS/ERP systems (e.g. IFS) and integrating maintenance / asset-management data into performance analytics.
6.  Experience working in or closely with a Remote Operations Centre / control room environment.
7.  Knowledge of energy measurement and verification (M&V) standards or methodologies and their application to performance analytics.