Systems Manager - GCP Data Engineering, Enterprise Data & Analytics

Con Edison; New York (NY); On-site; USD 170,000 - 210,000; Full time

Job summary

Con Edison is seeking a Systems Manager, GCP Data Engineering to lead the Enterprise Data & Analytics Platform team. You will guide the design, construction, and operation of enterprise-grade data pipelines on Google Cloud, ensuring reliability and governance.

The role combines hands-on technical depth in SQL, Python, Spark with strategic leadership, FinOps awareness, and a commitment to scalable, data-driven insights for business stakeholders and AI/ML deployments.

Qualifications

• Strong SQL, Python, and Spark experience for building scalable data pipelines.
• Deep knowledge of GCP-native serverless architectures and data governance.
• Proven track record in leading data engineering teams.

Responsibilities

• Lead a hands‑on engineering team responsible for EDAP data pipelines.
• Design, build, and maintain enterprise data pipelines on GCP.
• Collaborate with architects and stakeholders to deliver data products.

Skills

SQL

Python

Spark

GCP

Data Modeling

Leadership

Tools

BigQuery

Dataflow

Cloud Composer

Job description

The Systems Manager, GCP Data Engineering, Enterprise Data & Analytics, leads the hands‑on technical team responsible for designing, building, and maintaining the enterprise‑grade data pipelines that power the Enterprise Data & Analytics Platform (EDAP). This role manages a highly skilled team of engineers who own the core stages of the data lifecycle on Google Cloud Platform (GCP)including data transfer, ingestion, transformation, curation, exposure, and activation.

In this role, you will lead the strategic build-out of scalable data pipelines to enable reliable downstream data activation for business stakeholders, analytics, and AI/ML model deployment. You will collaborate directly with solution architects, business system owners, and platform partners to deliver high‑quality data products while maintaining hands‑on depth in SQL, Python, Spark, and GCP-native serverless architecture. The ideal candidate blends outstanding technical, data engineering, and data modeling expertise with a proven track record of people management, fostering a culture of operational excellence, FinOps cost‑efficiency, and rigorous data governance implementation.

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