About the position
A leading independent power producer (IPP) focused on developing, owning, and operating clean and reliable electricity generation across Africa is seeking a Data & ML Engineer who will drive the technical implementation of the company's Data Transformation initiative.
Responsibilities:
- Ingestion & Orchestration: Design and maintain modular pipelines (API, SQL, ETL/ELT) integrating sources like CAMs, ERP, OT/IoT, and SharePoint into the SSOT.
- Data Modeling: Develop robust schemas and feature pipelines supporting analytics, reporting, and ML.
- Operational Health: Manage domain monitoring, alerting, incident resolution, advanced SQL performance tuning, and schema design.
- Model Development: Build, evaluate, and fine-tune ML models (forecasting, anomaly detection, neural networks) using standard libraries, actively managing overfitting and drift.
- Pipeline Integration: Embed feature engineering and model scoring into automated data workflows.
- MLOps Delivery: Implement end-to-end MLOps for domain use cases, including versioning, CI/CD, deployment, monitoring, and automated retraining.
- Configuration: Own AVEVA PI and CAMs for the domain; design PI Asset Framework (AF) element templates, hierarchies, and internal calculations.
- Lifecycle Management: Oversee asset onboarding/offboarding, ensuring accurate data registration, configuration, and alignment with reporting frameworks.
- Integrations & Alerts: Configure notifications, event frames, and PI interfaces to maintain high data integrity across SSOT integrations.
- End-to-End Delivery: Function as single-point-of-contact for domain data products, making scalable architecture decisions and filling technical gaps independently.
- Standards & Compliance: Enforce data governance, security, IT standards, and structured change management (approvals, rollback plans).
- Collaboration & Mentorship: Translate complex technical concepts for non-technical stakeholders, coordinate cross-divisional timelines, review associate work, and mentor team members on emerging practices.
Minimum Requirements:
- Education: Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field.
- Experience: 5–7 years in data engineering or data platform development (5+ years acceptable with strong evidence of independent delivery ownership).
- Technical Experience: Practical hands-on ownership of API integration, ETL/ELT orchestration, data modeling, and production operations.
- Tooling: Strong SQL and Python skills, ETL/automation frameworks, and experience integrating ML models into production workflows.
- Programming & Querying: Strong Python and advanced SQL (schema design, indexing, performance tuning). C# is an advantage.
- Data Architecture: Proven track record designing scalable ingestion frameworks, integrations, and enterprise data models.
- ML & MLOps: Solid theoretical ML foundations combined with practical production deployment and MLOps practices.
- Industrial Platforms: Hands-on experience configuring AVEVA PI (Asset Framework, interfaces, event frames) and CAM platforms.
- Soft Skills: High autonomy, technical discipline, cross-functional communication, and stakeholder management.
- Advantageous
- AVEVA PI: Hands-on integration via PI Integrator or PI Web API / SDKs.
- Advanced ML: Production deep learning exposure (e.g., TensorFlow, PyTorch) and MLOps tooling.
- Big Data & Orchestration: Experience with Data Lakes, Azure Data Factory, Airflow, or Prefect.
- Domain & Low-Code: Energy/industrial IoT experience, plus familiarity with Power Apps and Power Automate.
Benefits:
Competitive salary based on experience (salary can potentially be more based on experience/skills)
IF you meet the above requirements and want to make a career-changing move, apply today by emailing your CV to [Email Address Removed].com
Desired Skills:
- Data & ML Engineer
- Data & ML Engineer
- Data & ML Engineer
