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Senior Data & ML Engineer at Hire Resolve
Hire Resolve
September 10, 2026
Full-time
On-site
Job Description
A South African company that manages and operates utility-scale renewable energy power plants across South Africa is seeking a Senior Data & ML Engineer who will set the technical direction and own the architecture for the company's enterprise data platform.
Responsibilities:
Design robust ingestion, orchestration, and integration architectures across ERP, OT/IoT, CAMs, and SharePoint systems, optimizing for scale, cost, resilience, and security.
Own the enterprise data model and SSOT design to ensure consistency across business domains.
Establish and enforce core engineering standards (testing, CI/CD pipelines, release, and rollback protocols) via rigorous code reviews.
Architect MLOps frameworks and ML data pipelines, defining standard patterns for algorithm development and library usage.
Design integration patterns to power ML models using operational (AVEVA PI) and asset management data.
Define and govern standards for the responsible adoption of AI and Generative AI tools.
Own the technical architecture for AVEVA PI and CAMs, ensuring high availability, scaling, and seamless upgrade planning.
Set fleetwide Asset Framework (AF) modeling, calculation, and mapping standards, driving analytics-led alerting and cross-site data integrity.
Resolve complex, cross-system architectural challenges and lead major incident reviews.
Enforce enterprise data governance, security, and model risk standards.
Align cross-divisional stakeholders and leadership on strategic technical decisions.
Mentor engineers, cultivate a culture of technical excellence, and drive team capability in emerging technologies.
Minimum Requirements:
Education: Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field.
Experience: 7 - 10 years in data engineering or data platform development (minimum 6 years with demonstrated platform ownership at a Senior/Lead level).
Technical Mastery: Advanced SQL and Python skills, hands-on experience with ETL/ELT, enterprise data modeling, and scalable MLOps.
Track Record: Proven experience leading platform architecture, enforcing engineering standards, and managing complex production environments.
Data Engineering & Architecture: Expert-level SQL, ETL/ELT pipelines, data modeling, and platform-wide architectural design.
ML & AI Infrastructure: Advanced capability in MLOps, production ML integration, and AI readiness.
OT & Asset Systems: In-depth technical mastery of AVEVA PI, Asset Framework (AF) modeling, and CAMs integration.
Reliability & Governance: Strong focus on operational discipline, system availability, and risk management.
Leadership: Proven ability to influence executive leadership, align cross-functional teams, and mentor junior engineers.
Advantageous
Enterprise-level AVEVA PI architecture (HA, upgrades, AF modeling) in industrial/energy sectors.
Production ML frameworks (e.g., PyTorch, TensorFlow) and orchestration tools (e.g., Azure Data Factory, Airflow, Prefect).
Cloud data lake/big data architectures and formal AI/ML governance frameworks.
A South African company that manages and operates utility-scale renewable energy power plants across South Africa is seeking a Senior Data & ML Engineer who will set the technical direction and own the architecture for the company's enterprise data platform.
Responsibilities:
Design robust ingestion, orchestration, and integration architectures across ERP, OT/IoT, CAMs, and SharePoint systems, optimizing for scale, cost, resilience, and security.
Own the enterprise data model and SSOT design to ensure consistency across business domains.
Establish and enforce core engineering standards (testing, CI/CD pipelines, release, and rollback protocols) via rigorous code reviews.
Architect MLOps frameworks and ML data pipelines, defining standard patterns for algorithm development and library usage.
Design integration patterns to power ML models using operational (AVEVA PI) and asset management data.
Define and govern standards for the responsible adoption of AI and Generative AI tools.
Own the technical architecture for AVEVA PI and CAMs, ensuring high availability, scaling, and seamless upgrade planning.
Set fleetwide Asset Framework (AF) modeling, calculation, and mapping standards, driving analytics-led alerting and cross-site data integrity.
Resolve complex, cross-system architectural challenges and lead major incident reviews.
Enforce enterprise data governance, security, and model risk standards.
Align cross-divisional stakeholders and leadership on strategic technical decisions.
Mentor engineers, cultivate a culture of technical excellence, and drive team capability in emerging technologies.
Minimum Requirements:
Education: Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field.
Experience: 7 - 10 years in data engineering or data platform development (minimum 6 years with demonstrated platform ownership at a Senior/Lead level).
Technical Mastery: Advanced SQL and Python skills, hands-on experience with ETL/ELT, enterprise data modeling, and scalable MLOps.
Track Record: Proven experience leading platform architecture, enforcing engineering standards, and managing complex production environments.
Data Engineering & Architecture: Expert-level SQL, ETL/ELT pipelines, data modeling, and platform-wide architectural design.
ML & AI Infrastructure: Advanced capability in MLOps, production ML integration, and AI readiness.
OT & Asset Systems: In-depth technical mastery of AVEVA PI, Asset Framework (AF) modeling, and CAMs integration.
Reliability & Governance: Strong focus on operational discipline, system availability, and risk management.
Leadership: Proven ability to influence executive leadership, align cross-functional teams, and mentor junior engineers.
Advantageous
Enterprise-level AVEVA PI architecture (HA, upgrades, AF modeling) in industrial/energy sectors.
Production ML frameworks (e.g., PyTorch, TensorFlow) and orchestration tools (e.g., Azure Data Factory, Airflow, Prefect).
Cloud data lake/big data architectures and formal AI/ML governance frameworks.