Systems Manager - GCP Data Engineering, Enterprise Data & Analytics

ConEdison•New York, NY

3d•$145,000 - $195,000

About The Position

The Systems Manager, GCP Data Engineering, Enterprise Data & Analytics leads the hands-on technical team responsible for designing, building, and maintaining enterprise-grade data pipelines for the Enterprise Data & Analytics Platform (EDAP). The team owns core data lifecycle stages on Google Cloud Platform (GCP), including data transfer, ingestion, transformation, curation, exposure, and activation. The role leads the strategic development of scalable pipelines that enable reliable downstream data activation for business stakeholders, analytics, and AI/ML model deployment. It collaborates with solution architects, business system owners, and platform partners to deliver high-quality data products, while providing technical depth in SQL, Python, Spark, and GCP-native serverless architecture. The manager fosters operational excellence, FinOps cost-efficiency, and rigorous data governance. The position includes an assigned System Emergency Assignment and may require work outside business hours during emergencies.

Requirements

Master's degree and at least 6 years of relevant full-time work experience, or bachelor's degree and at least 8 years of relevant full-time work experience.
Hands-on data engineering, pipeline architecture, or data platform operations experience, including leading and managing technical teams.
Extensive hands-on experience designing, developing, and operating large-scale GCP data pipelines using services such as BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex, and Cloud Storage.
Proficiency in SQL, Python, or Spark for large-scale data processing and data modeling.
Experience with ETL/ELT pipeline design, workflow orchestration such as Composer/Airflow, CI/CD pipelines, and Infrastructure-as-Code such as Terraform.
Experience defining and managing operational metrics, SLAs, and KPIs for enterprise-level data integration platforms.
Effective leadership, written and verbal communication, problem-solving, time management, and priority-setting skills.
Flexibility to work in a fast-paced, dynamic environment and manage multiple assignments while remaining organized and detail-oriented.
Driver's license required.
Ability to sit or stand while using a keyboard, mouse, and computer for the duration of the workday.
Available to work off hours as operationally required, including nights, weekends, and holidays during emergencies.

Nice To Haves

Master's degree in Computer Science, Engineering, Math, Business, or a technology-centric field, with at least 6 years of relevant full-time experience in GCP data engineering, scalable pipelines, team management, and governed, cost-efficient analytics and AI/ML data products.
Familiarity with data governance frameworks, metadata management, and data quality testing tools such as Great Expectations or Dataplex.
Experience in utilities, energy, or other regulated industries, including NERC CIP compliance, security protocols, and data classification.
Experience supporting migrations of data workloads from legacy ecosystems such as Azure, Databricks, and C3.ai to GCP.
Google Professional Data Engineer certification.
Google Professional Cloud Architect certification.
Technical certifications in IT.

Responsibilities

Lead, mentor, and develop a high-performing team of GCP data engineers; establish work plans and performance metrics and manage day-to-day agile execution.
Provide hands-on technical leadership, including guiding architecture, reviewing code, prototyping, troubleshooting, and resolving complex pipeline issues.
Oversee secure onboarding of batch and streaming datasets from legacy on-premises systems and cloud sources into the platform landing zone.
Drive the design and development of performant, automated transformations and trusted datasets using a structured Bronze, Silver, and Gold medallion architecture and tools such as SQL, Python, and Dataform.
Lead delivery of secure exposure layers, including views, authorized datasets, and APIs, for business consumers, dashboards, and AI systems such as Enterprise Gemini, Vertex AI, and Model & Agent Garden.
Establish monitoring, alerting, and performance-tuning frameworks to ensure resilient, highly available, cost-efficient data pipelines; work with FinOps to identify spending anomalies and optimize GCP query and storage costs.
Implement and operationalize data quality rules, data classification, lineage tracking, and access controls with the Data Governance team, supporting compliance with privacy rules and NERC CIP.
Coordinate across data engineering, platform operations, legacy migration workstreams, and business stakeholders to align platform capabilities with use cases.
Work off hours as operationally required, including non-business hours during emergencies that may include nights, weekends, and holidays.