Systems Manager - GCP Data Engineering, Enterprise Data & Analytics

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.

Core Responsibilities

• Team Leadership & Development: Lead, mentor, and develop a technical, high-performing team of GCP data engineers, establishing work plans, setting performance metrics, and managing day-to-day agile execution
• Hands-on Technical Leadership: Maintain deep technical expertise to guide architecture, review code, and directly assist with prototyping, troubleshooting, and unblocking complex pipeline issues
• Data Ingestion & Transfer: Oversee the secure onboarding of diverse batch and streaming datasets from legacy on-premises systems and cloud sources into the landing zone of the platform
• Data Transformation & Curation: Drive the design and development of performant, automated data transformations, establishing trusted, clean datasets via a structured medallion architecture (Bronze, Silver, Gold layers) using tools such as SQL, Python, and Dataform
• Data Exposure & Activation: Lead the delivery of secure exposure layers (views, authorized datasets, APIs) that surface curated data products to business consumers, dashboard interfaces, and advanced AI systems (Enterprise Gemini/Vertex AI / Model & Agent Garden)
• Pipeline Reliability & FinOps: Establish monitoring, alerting, and performance-tuning frameworks to ensure data pipelines are resilient, highly available, and cost-efficient. Coordinate closely with FinOps to identify spend anomalies and optimize GCP query and storage costs
• Governance & Compliance Alignment: Implement and operationalize data quality rules, data classification, lineage tracking, and access controls in tight integration with the Data Governance team, ensuring compliance with privacy rules (PII, NERC CIP)