Data​/AI Expert

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 & Data Quality), Power BI, and OT/industrial data systems including PI System, SCADA, and DCS.

• 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.

• Demonstrates deep, hands‑on expertise in enterprise data engineering, AI/ML platform development, and cloud-native architectures within highly regulated environments.
• Maintains current knowledge of global trends in data engineering, MLOps, GenAI, cloud technologies (Azure, hybrid, on-premise), and nuclear industry compliance requirements.
• Understands the full lifecycle of data and AI platforms including design, development, testing, deployment, monitoring, and decommissioning.
• Applies comprehensive knowledge of data governance frameworks (Collibra, Unity Catalog), master data management, data quality, and lineage management.
• Possesses strong awareness of OT/IT convergence, industrial data systems (PI System, SCADA, DCS), and their integration into enterprise analytics environments.
• Maintains expert-level proficiency in programming languages and frameworks including Python, SQL, Spark, and relevant AI/ML libraries.

• Tackles complex, ambiguous engineering challenges in data platform architecture, AI model development, and system integration—delivering innovative, compliant, and production-ready solutions.
• Leads root cause analysis and corrective/preventive actions for platform incidents, data quality failures, model degradation, and system disruptions.
• Develops and implements robust engineering solutions to address performance bottlenecks, scalability constraints, and integration challenges.
• Navigates conflicting technical requirements, evolving regulatory standards, and stakeholder needs to deliver optimal engineering outcomes.
• Drives continuous improvement by benchmarking against global engineering best practices, evaluating emerging technologies, and implementing lessons learned.

• Min:
  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).
• Pref:
  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.

• Min:
  Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
• Pref:
  Master’s degree in a relevant field.

• Min:
  Minimum 12+ years’ hands-on experience in enterprise data engineering, AI/ML platform development, and cloud-native architectures, including production deployment and operations.
• Embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
• Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors (Pref).
• Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in Databricks or similar AI/analytics platforms (Pref).
• Demonstrated experience leading large-scale data and AI engineering programs (Pref).