Qureos Mentor

ENEC Operations

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 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 (Delta Lake, Unity Catalog, MLflow, Delta Live Tables), Microsoft Azure (Azure Data Factory, Azure ML, Microsoft Fabric, Azure AI Foundry, Copilot Studio), Collibra (Data Governance &amp; Data Quality), 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 generation. This role acts as a principal technical contributor and mentor, enabling operational excellence and digital transformation at scale.
 
 
 Activity: Data Engineering Architecture &amp; Platform Development
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; 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).
 
 
 &bull; Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools.
 
 
 &bull; Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs.
 
 
 &bull; Ensure seamless integration of OT and industrial data sources &mdash; including PI System, SCADA, and DCS &mdash; into enterprise data platforms.
 
 
 &bull; Design and implement APIs, data contracts, and integration frameworks to enable enterprise-wide data consumption.
 
 
 &bull; Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements.
 
 
 &bull; Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog.
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design, develop, and deploy production-grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML.
 
 
 &bull; Engineer end-to-end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining &mdash; ensuring reliability and reproducibility.
 
 
 &bull; 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.
 
 
 &bull; Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self-service analytics and executive reporting across the organization.
 
 
 &bull; Apply Responsible AI principles and governance frameworks to all AI and ML solutions, ensuring explainability, fairness, and compliance.
 
 
 &bull; Conduct rigorous model evaluation, performance benchmarking, and validation to ensure solutions meet accuracy, reliability, and safety standards.
 
 
 &bull; Contribute to the continuous improvement of AI engineering standards, patterns, and reusable components across the enterprise.
 
 
 Activity: Advanced Subject Matter Knowledge
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Demonstrates deep, hands-on expertise in enterprise data engineering, AI/ML platform development, and cloud-native architectures within highly regulated environments.
 
 
 &bull; Maintains current knowledge of global trends in data engineering, MLOps, GenAI, cloud technologies (Azure, hybrid, on-premise), and nuclear industry compliance requirements.
 
 
 &bull; Understands the full lifecycle of data and AI platforms including design, development, testing, deployment, monitoring, and decommissioning.
 
 
 &bull; Applies comprehensive knowledge of data governance frameworks (Collibra, Unity Catalog), master data management, data quality, and lineage management.
 
 
 &bull; Possesses strong awareness of OT/IT convergence, industrial data systems (PI System, SCADA, DCS), and their integration into enterprise analytics environments.
 
 
 &bull; Maintains expert-level proficiency in programming languages and frameworks including Python, SQL, Spark, and relevant AI/ML libraries.
 
 
 Activity: Complex Problem Solving &amp; Innovation
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Tackles complex, ambiguous engineering challenges in data platform architecture, AI model development, and system integration &mdash; delivering innovative, compliant, and production-ready solutions.
 
 
 &bull; Leads root cause analysis and corrective/preventive actions for platform incidents, data quality failures, model degradation, and system disruptions.
 
 
 &bull; Develops and implements robust engineering solutions to address performance bottlenecks, scalability constraints, and integration challenges.
 
 
 &bull; Navigates conflicting technical requirements, evolving regulatory standards, and stakeholder needs to deliver optimal engineering outcomes.
 
 
 &bull; Drives continuous improvement by benchmarking against global engineering best practices, evaluating emerging technologies, and implementing lessons learned.
 
 
 &bull; Leads proof-of-concept and pilot initiatives to evaluate new data and AI technologies and engineering approaches
 
 
 
 
 
 Activity: Applied Skills &amp; Professional Know-How
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Expertly engineers, deploys, and operates scalable, secure, and compliant data and AI platforms across Databricks, Azure, and OT environments.
 
 
 &bull; Applies hands-on proficiency in building data pipelines, ML models, GenAI solutions, BI semantic models, and governance frameworks in production environments.
 
 
 &bull; Mentors and develops junior and mid-level data engineers, data scientists, and analytics engineers &mdash; building future-ready technical capabilities.
 
 
 &bull; Leads technical design reviews, code reviews, and architecture assessments to uphold engineering quality and best practices.
 
 
 &bull; Effectively translates complex technical concepts into clear, actionable insights for both technical and non-technical stakeholders.
 
 
 &bull; Documents solutions, engineering patterns, and technical standards to build institutional knowledge and enable knowledge transfer.
 
 
 Activity: Security, Governance &amp; Compliance
 
 
 
 
 
 Responsibilities and Accountabilities:
 
 
 &bull; Design and enforce data security, access control, and privacy frameworks across all data and AI platforms using Unity Catalog, Collibra, and Azure security controls.
 
 
 &bull; Ensure all data engineering and AI solutions comply with national and international nuclear regulations, FANR requirements, and Responsible AI principles.
 
 
 &bull; Maintain comprehensive data lineage, audit trails, and documentation to support regulatory, audit, and governance requirements.
 
 
 &bull; Implement and enforce data quality frameworks and data governance policies using Collibra Data Quality and Unity Catalog.
 
 
 &bull; Conduct regular platform security assessments, data quality audits, and compliance reviews.
 
 
 &bull; Oversee risk management, audit readiness, and the implementation of robust technical controls for all platform operations.
 
 
 
 
 
 
 
 
 
 Minimum
 
 
 
 &bull; Bachelor&rsquo;s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
 
 
 &bull; Minimum 12+ years' hands-on experience in enterprise data engineering, AI/ML platform development, and cloud-native architectures, including production deployment and operations.
 
 
 &bull; Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
 
 
 
 
 
 
 
 
 
 Preferred
 
 
 
 
 
 
 &bull; Master&rsquo;s degree in a relevant field.
 
 
 &bull; Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors.
 
 
 &bull; Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in data bricks or similar AI/analytics platforms exposure.
 
 
 &bull; Demonstrated experience leading large-scale data and AI engineering programs
 
 
 
 
 
 
 
 
 
 Certification -
 
 
 
 
 
 
 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.
 
 
 
 
 
 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).
 
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Data and AI Engineering Expert

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Data and AI Engineering Expert