Systems Manager C3.ai & Azure Databricks, Enterprise Data & Analytics

Overview

The Systems Manager, C3.ai & Azure Databricks, Enterprise Data & Analytics, leads the strategy, delivery, and operational excellence of Con Edisons enterprise data platform on C3.ai (hosted on Google Cloud) and Azure Databricks. This role provides end-to-end ownership of data ingestion, governance, and platform performance to ensure that enterprise data is reliable, secure, and ready to support analytics, regulatory reporting, and advanced AI/ML initiatives.

The Systems Manager oversees the engineering, architecture, and operations of both platforms and associated data intake services, ensuring adherence to enterprise standards for reliability, scalability, and cost efficiency. This includes managing the full data lifecycle from intake and transformation to consumption while maintaining strong controls around data quality, security, and privacy. Partnering with Data Governance, Analytics, AI/ML, and Application teams, this role translates enterprise data strategy into production-grade solutions that power business insights and grid modernization. The position also manages internal staff and external vendors to deliver continuous improvement and operational resilience across Con Edisons data ecosystem.

This position does not provide employment pursuant to the terms of a STEM OPT Training Plan.

Responsibilities

• Own and manage the enterprise data platforms on C3.ai and Azure Databricks, including datasets, schemas, environments, and supporting services
• Lead and oversee enterprise data intake, including batch and streaming ingestion pipelines, ensuring reliability, scalability, and timeliness
• Establish standardized data onboarding patterns to enable rapid and consistent delivery of new data sources and data products
• Define and enforce platform standards for data modeling, partitioning, clustering, transformation patterns, and development best practices
• Enable analytics, reporting, and data engineering teams to develop and deploy data products efficiently
• Ensure day-to-day operational excellence of the enterprise analytics platform, including monitoring, alerting, incident response, and root cause analysis
• Partner with Data Governance and Security teams to enforce data access controls, privacy requirements, retention policies, and auditability
• Ensure data quality controls and validation processes are embedded within ingestion and transformation workflows
• Monitor platform resource usage and consumption, implementing cost controls and optimization strategies
• Manage, lead and develop platform engineering and operations staff supporting analytics platforms and data intake services through coaching, performance management, and effective work assignment
• Execute the enterprise data platform roadmap by operationalizing approved standards and architectural decisions
• Identify opportunities to improve platform reliability, automation, and developer experience
• Support Agile delivery practices, backlog prioritization, and continuous improvement initiatives across the data platform

Qualifications

• Master's Degree in Computer Science, Engineering, Math, Business, or technology-centric field and a minimum of 6 years relevant full-time work experience or
• Bachelor's Degree in Computer Science, Engineering, Math, Business, or technology-centric field and a minimum of 8 years relevant full-time work experience

• Demonstrated experience leading enterprise data platform strategy, engineering, and operations on cloud infrastructure providers / platforms, with deep expertise in C3.ai (hosted on Google Cloud), Azure Databricks, or both, required
• Proven end-to-end ownership of enterprise data platforms, including data ingestion, transformation, governance, and performance management, required
• Ability to communicate platform health, risks, cost posture, and delivery progress to senior leadership and enterprise stakeholders, required
• Hands on experience overseeing data architecture, capacity planning, performance optimization, and cost management in large scale environments, required
• Strong background designing and operating scalable, reliable, and cost-efficient data platforms that support analytics, regulatory reporting, and AI and ML initiatives, preferred
• Demonstrated ability to enforce enterprise standards for data quality, security, privacy, and compliance across the full data lifecycle, preferred
• Experience partnering closely with Data Governance, Analytics, AI and ML, and Application teams to translate enterprise data strategy into production grade solutions, preferred
• Proven track record implementing and operating secure data ingestion and intake services with strong controls for access management, encryption, and auditability, preferred
• Experience managing internal engineering teams and external vendors, including delivery planning, performance management, and continuous improvement initiatives, preferred
• Demonstrated experience operating mission critical data platforms with high availability, operational resilience, and well-defined incident and recovery processes, preferred

• Strong written and verbal communication skills
• Builds and manages effective teams
• Possesses flexibility to work in a fast paced, dynamic environment
• Ability to work within tight timeframes and meet strict deadlines
• Demonstrated problem solving skills
• Effective interpersonal skills
• Ability to drive multiple projects to successful completion
• Demonstrated time management and priority setting skills
• Ability to simultaneously handle multiple priorities
• Well organized, detail oriented and flexible to handle multiple assignments

• Driver's License Required
• Other: Technical certification(s) in IT Preferred

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

• 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.
• Available to work off hours as operationally required