Data and AI Engineering Expert
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Key skills for this role
About the Role
ENEC Operations is seeking a senior hands-on Data and AI Engineering Expert to design, develop, and operationalize enterprise data and AI platforms.
Key Skills for This Role
Responsibilities
- Design, build, and maintain scalable, secure, and high performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables
- Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools
- Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs
- Ensure seamless integration of OT and industrial data sources — including PI System, SCADA, and DCS — into enterprise data platforms
- Design and implement APIs, data contracts, and integration frameworks to enable enterprise wide data consumption
- Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements
- Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog
- Design, develop, and deploy production grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML
- Engineer end to end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining
- Build and operationalize AI powered solutions including predictive analytics, anomaly detection, NLP, and GenAI/LLM based applications using Azure AI Foundry and Copilot Studio
- Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self service analytics and executive reporting
- Apply Responsible AI principles and governance frameworks to all AI and ML solutions
Requirements
- Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline
- Minimum 12+ years' hands on experience in enterprise data engineering, AI/ML platform development, and cloud native architectures, including production deployment and operations
- Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains
- Master’s degree in a relevant field
- Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors
- Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in Databricks or similar AI/analytics platforms
- Demonstrated experience leading large scale data and AI engineering programs
- Relevant certifications in Databricks, Microsoft Azure, MLOps, or Data Governance (Collibra)
Full Job Posting
Role Overview
- The Data and AI Engineering Expert is a senior hands on technical authority responsible for the design, development, and operationalization of enterprise data and AI platforms across ENEC.
- This role leads the engineering delivery of scalable, secure, and compliant data pipelines, AI/ML solutions, and analytics platforms leveraging technologies such as Databricks, Microsoft Azure, Collibra, Power BI, and OT/industrial data systems including PI System, SCADA, and DCS.
- The Expert applies deep engineering expertise to architect and deliver production grade solutions, ensures data governance and quality standards are upheld, and drives the adoption of AI and analytics capabilities that directly support ENEC's mission of safe, innovative, and efficient nuclear energy
- This role acts as a principal technical contributor and mentor, enabling operational excellence and digital transformation at scale.
Responsibilities and Accountabilities
- Design, build, and maintain scalable, secure, and high performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables (DLT).
- Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools.
- Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs.
- Ensure seamless integration of OT and industrial data sources — including PI System, SCADA, and DCS — into enterprise data platforms.
- Design and implement APIs, data contracts, and integration frameworks to enable enterprise wide data consumption.
- Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements.
- Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog.
- Design, develop, and deploy production grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML.
- Engineer end to end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining — ensuring reliability and reproducibility.
- Build and operationalize AI powered solutions including predictive analytics, anomaly detection, natural language processing (NLP), and GenAI/LLM based applications using Azure AI Foundry and Copilot Studio.
- Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self service analytics and executive reporting across the organization.
- Apply Responsible AI principles and governance frameworks to all AI and ML solutions, ensuring explainability, fairness, and compliance.
Education and Experience
- Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
- Minimum 12+ years' hands on experience in enterprise data engineering, AI/ML platform development, and cloud native architectures, including production deployment and operations.
- Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
- Master’s degree in a relevant field.
- Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors.
- Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in Databricks or similar AI/analytics platforms exposure.
- Demonstrated experience leading large scale data and AI engineering programs.
Certification
- Relevant certifications in one or more of: Databricks (Data Engineer Associate/Professional, ML Professional), Microsoft Azure (Data Engineer, AI Engineer, Solutions Architect), MLOps, or Data Governance (Collibra).
- Multiple cloud and AI certifications. Microsoft Fabric, Azure AI Foundry, or Databricks Unity Catalog specialty certifications. Industry publications or contributions in data engineering or AI.
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