This role is responsible for gathering, engineering, processing, and analyzing data from diverse systems and platforms to generate actionable insights that support strategic and operational decision-making within the Company.

The role designs and maintains scalable data pipelines, develops advanced analytics and Artificial Intelligence (AI) models (including machine learning and predictive analytics), and supports data-driven innovation initiatives such as predictive maintenance, demand forecasting, operational optimization, and smart grid analytics.

Additionally, the role ensures high data quality standards, contributes to enterprise data architecture, supports AI governance frameworks, and continuously explores emerging data technologies to enhance business value.

The position will report to the Manager – Digital Innovations.

Key Accountabilities

• Design, develop and maintain scalable data pipelines (ETL/ELT processes) to ingest data from multiple enterprise and operational systems (IT & OT environments).
• Identify and integrate structured and unstructured data sources into the enterprise data warehouse or data lake.
• Collaborate with stakeholders to define data requirements aligned to business objectives.
• Automate data ingestion and transformation workflows to improve efficiency and reliability.
• Support integration of IoT, SCADA, and other data sources into analytics platforms.
• Contribute to data warehouse and data lake architecture design (e.g., Kimball, Inmon, Lakehouse approaches).
• Manage and optimize data marts to meet evolving business intelligence needs.
• Ensure query optimization and performance tuning for large datasets.
• Support cloud-based or hybrid data infrastructure where applicable.
• Clean, preprocess, and validate datasets to ensure accuracy, completeness, and consistency.
• Implement data quality checks and monitoring frameworks.
• Maintain documentation of data definitions, lineage, and metadata.
• Support data governance, privacy, and AI ethics compliance initiatives.
• Conduct exploratory data analysis (EDA) to identify trends and relationships.
• Develop, train, test, and deploy machine learning models for:
  • Predictive maintenance
  • Demand forecasting
  • Load profiling
  • Risk detection
  • Asset performance optimization
• Implement AI and statistical models using supervised and unsupervised learning techniques.
• Evaluate and monitor model performance, accuracy, bias, and drift.
• Research and evaluate emerging AI technologies for business adoption.
• Support development of Generative AI or automation use cases where applicable.
• Develop interactive dashboards and real-time analytics solutions.
• Use tools such as Power BI, Tableau, Python libraries or equivalent platforms.
• Translate complex analytical findings into clear business insights.
• Prepare executive-level reports and presentations for decision-making forums.
• Support Smart Grid and Digital Transformation initiatives through advanced analytics.
• Identify opportunities for automation and intelligent decision systems.
• Collaborate across all CEC teams on data-driven initiatives.
• Demonstrate consistent behavior in line with the Company’s Health, Safety, Environment and Risk Management policies.
• Identify and manage risks related to data security, AI deployment, and operational impact.
• Support effective utilization of analytics tools and infrastructure within approved budgets.
• Identify opportunities for process optimization and cost reduction through data-driven insights.

To be successful in this role, you will be required to have:

• Grade 12 Certificate.
• Bachelor’s Degree in: Computer Science, Data Science, Data Engineering, Artificial Intelligence, Information Technology or equivalent.
• Professional certifications in Data Analytics, AI, Machine Learning, or Cloud Platforms will be an added advantage.
• Membership of EIZ, ICTAZ.
• At least 3 years’ experience in:
  • Data analytics and modeling
  • Database management systems
  • Data engineering and pipeline development
  • Machine learning or AI model development
• Experience working with large datasets, preferably in utility, telecoms, or critical infrastructure environments will be an added advantage.
• Strong technical expertise in one or more programming languages such as Python and R.
• Strong SQL skills and experience with relational and non-relational databases.
• Experience with ETL/ELT tools and data pipeline development.
• Experience working with data visualization tools such as Tableau, Power BI or libraries such as matplotlib and seaborn in Python.
• Experience in Machine Learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch), Model validation and performance monitoring.
• Understanding of AI governance, ethics, and model risk management.
• Advanced skills in Microsoft Excel, including the use of formulas, pivot tables, and data analysis tools.
• Familiarity with version control (e.g., Git) and collaborative development workflows.
• Adaptability to learn and research new technologies.