Systems Manager - GCP Data Engineering, Enterprise Data & Analytics

Overview

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.

Responsibilities

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)
• Collaborative Partnering: Act as the key liaison between data engineering, platform operations, legacy migration workstreams, and business stakeholders to align platform capabilities with immediate use cases

Qualifications

Required Education/Experience

• Master's Degree and a minimum of 6 years relevant full-time work experience or
• Bachelor's Degree and a minimum of 8 years relevant full-time work experience

Preferred Education/Experience

• Master's Degree in Computer Science, Engineering, Math, Business, or a technology-centric field and a minimum of 6 years of relevant full-time work experience in GCP data engineering building scalable data pipelines, managing skilled teams, and delivering governed, cost-efficient data products for analytics and AI/ML

Relevant Work Experience

• Demonstrated experience in hands-on data engineering, pipeline architecture, or data platform operations, including time spent leading and managing technical teams, required
• Extensive hands-on experience designing, developing, and operating large-scale data pipelines on Google Cloud Platform, using services like BigQuery, Dataflow, Dataproc, Pub/Sub, Dataplex, and Cloud Storage, required
• Proficient in SQL, Python, or Spark for large-scale data processing and data modeling, requiredSolid experience with ETL/ELT pipeline design, workflow orchestration (e.g., Composer/Airflow), CI/CD pipelines (Azure DevOps preferred), and Infrastructure-as-Code (Terraform), required
• Experience defining and managing operational metrics, SLAs, and KPIs for enterprise-level data integration platforms, required
• Familiarity with implementing data governance frameworks, metadata management, and data quality testing tools (e.g., Great Expectations, Dataplex), preferred
• Background in utilities, energy, or other regulated industries with exposure to NERC CIP compliance, security protocols, and data classification, preferred
• Experience supporting migration initiatives moving data workloads off legacy ecosystems (such as Azure, Databricks, and C3.ai) onto GCP, preferred

Skills and Abilities

• Effective leadership skills
• Strong written and verbal communication skills
• Possesses flexibility to work in a fast paced, dynamic environment
• Demonstrated problem solving skills
• Demonstrated time management and priority setting skills
• Well organized, detail oriented and flexible to handle multiple assignments

Licenses and Certifications

• Driver's License Required
• Other: Google Professional Data Engineer Preferred
• Accredited Asset Management Specialist (AAMS) Google Professional Cloud Architect Preferred
• Accredited Asset Management Specialist (AAMS) Technical certification(s) in IT 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.
• Available to work off hours as operationally required