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Overview The Systems Manager, AI Data Engineering, leads the development, operation, and scaling of secure, reliable, and high-performance data pipelines and platforms that power Con Edison's analytics, AI, and operational systems. This role ensures data is accurate, timely, and production-ready to support critical functions such as grid operations, asset management, forecasting, and customer analytics. The manager oversees all aspects of enterprise data engineering, including ingestion, transformation, storage, and delivery, ensuring that systems meet utility-grade standards for reliability, data quality, security, and compliance.Working at the intersection of data platforms and AI delivery, the Systems Manager, AI Data Engineering, collaborates with AI, governance, cybersecurity, and business teams to standardize and optimize data pipelines across the enterprise. The role embeds data quality controls and observability within pipelines to enable regulatory reporting, operational decision-making, and AI lifecycle management. By delivering curated, scalable, and trusted datasets, this position empowers advanced analytics, machine learning, and AI initiatives that drive safe, explainable, and reliable business outcomes.Responsibilities Core ResponsibilitiesLead the design, development, and operation of enterprise-grade data engineering pipelines that support analytics, machine learning, and AI use cases across Con Edisons business and operational domainsBuild and scale batch and streaming data ingestion frameworks integrating enterprise, operational, and external data sources into trusted, production-ready environmentsDevelop and maintain ETL/ELT processes, data models, and transformations that ensure accuracy, consistency, and performance across data lakes, warehouses, and feature storesImplement robust technical data quality rules, including schema validation, range checks, and automated reconciliation to maintain integrity and auditabilityCapture and manage metadata and lineage across all ingestion and transformation processes to support transparency, compliance, and explainable AIEnforce enterprise security standards through encryption, access controls, and role-based data permissions that protect sensitive operational and customer informationEstablish data observability practices for pipeline health, monitoring, alerting, and performance metrics to drive operational reliability and efficiencyCollaborate with data science and AI teams to build reusable feature engineering pipelines and scalable compute environments that enable model development and deploymentPartner with platform, governance, and cybersecurity teams to ensure all data operations comply with regulatory, privacy, and audit requirements in a utility-grade environmentContinuously improve pipeline reliability, scalability, and cost efficiency through automation, optimization, and modern engineering practicesLead and develop a team through coaching, performance management, and effective work assignment to drive aligned execution and business outcomeQualifications Required Education/ExperienceMaster's Degree and a minimum of 6 years full-time relevant work experience orBachelor's Degree and a minimum of 8 years of full-time relevant work experience.Preferred Education/ExperienceMaster's Degree in Business Administration, Finance, Accounting, Management Information Systems, Information Systems, related business or technology aligned field and a minimum of 6 years full-time relevant work experienceRelevant Work ExperienceDemonstrated experience leading enterprise data engineering teams responsible for building, operating, and scaling secure and reliable data pipelines in regulated environments, preferredProven ownership of end-to-end data engineering platforms, including data ingestion, transformation, storage, and delivery supporting analytics, AI, and operational systems, preferredStrong background designing production grade da