Systems Manager, AI Data Engineering

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.

- Lead 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 domains

- Build and scale batch and streaming data ingestion frameworks integrating enterprise, operational, and external data sources into trusted, production-ready environments

- Develop and maintain ETL/ELT processes, data models, and transformations that ensure accuracy, consistency, and performance across data lakes, warehouses, and feature stores

- Implement robust technical data quality rules, including schema validation, range checks, and automated reconciliation to maintain integrity and auditability

- Capture and manage metadata and lineage across all ingestion and transformation processes to support transparency, compliance, and explainable AI

- Enforce enterprise security standards through encryption, access controls, and role-based data permissions that protect sensitive operational and customer information

- Establish data observability practices for pipeline health, monitoring, alerting, and performance metrics to drive operational reliability and efficiency

- Collaborate with data science and AI teams to build reusable feature engineering pipelines and scalable compute environments that enable model development and deployment

- Partner with platform, governance, and cybersecurity teams to ensure all data operations comply with regulatory, privacy, and audit requirements in a utility-grade environment

- Continuously improve pipeline reliability, scalability, and cost efficiency through automation, optimization, and modern engineering practices

- Lead and develop a team through coaching, performance management, and effective work assignment to drive aligned execution and business outcome

Qualifications

Required Education/Experience

- Master's Degree and a minimum of 6 years full-time relevant work experience or

- Bachelor's Degree and a minimum of 8 years of full-time relevant work experience.

Preferred Education/Experience

- Master'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 experience

Relevant Work Experience

- Demonstrated experience leading enterprise data engineering teams responsible for building, operating, and scaling secure and reliable data pipelines in regulated environments, preferred

- Proven ownership of end-to-end data engineering platforms, including data ingestion, transformation, storage, and delivery supporting analytics, AI, and operational systems, preferred

- Strong background designing production grade data pipelines that meet utility grade standards for reliability, performance, data quality, security, and compliance, preferred

- Hands on experience supporting data pipelines for grid operations, asset management, forecasting, and customer analytics, preferred

- Demonstrated ability to embed data quality controls, validation, and observability directly into data pipelines to support regulatory reporting and operational decision making, preferred

- Experience collaborating closely with AI, data science, governance, cybersecurity, and business teams to standardize and optimize enterprise data pipelines, preferred

- Proven track record delivering curated, trusted, and scalable datasets that support machine learning, AI lifecycle management, and advanced analytics, preferred

- Experience implementing secure data access patterns, encryption, and privacy controls across enterprise data engineering platforms, preferred

- Demonstrated experience operating mission critical data systems with strong incident management, monitoring, and continuous improvement practices, preferred

- Ability to translate enterprise data and AI strategy into actionable data engineering roadmaps that deliver measurable business value, preferred

Skills and Abilities

- Strong written and verbal communication skills

- Builds and manages effective teams

- Ability to drive multiple projects to successful completion

- Ability to work within tight timeframes and meet strict deadlines

Licenses and Certifications

- Driver's License Required

- Project Management Professional (PMP) Preferred

- Certified Project Management (CPM) Preferred

Physical Demands

- Sit or stand to use a keyboard, mouse, and computer for the duration of the workday

Additional Physical Demands

- The selected candidate will be assigned a System Emergency Assignment (i.e., an emergency response role) and will be expected to work non-business hours during emergencies, which may include nights, weekends, and holidays.