Job DescriptionImmediate FT opening with our direct client in NYC\\n
This is a hybrid role offering salary plus bonus and benefits\\n
3 days a week on site required\\n
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The Systems Manager, Google Cloud Platform 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 (Google Cloud Platform)-including data transfer, ingestion, transformation, curation, exposure, and activation.
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Responsibilities\\n
\\n - Team Leadership & Development: Lead, mentor, and develop a technical, high-performing team of Google Cloud Platform data engineers (Band 2 & 3), establishing work plans, setting performance metrics, and managing day-to-day agile execution.
\\n - Hands-on Technical Leadership: Maintain deep technical expertise to guide architecture, review code, and directly assist with prototyping, troubleshooting, and unblocking complex pipeline issues.
\\n - 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.
\\n - 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.
\\n - 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).
\\n - 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 Google Cloud Platform query and storage costs.
\\n - 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).
\\n - 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.
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Qualifications\\n
\\n - Required 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; OR Bachelor\'s Degree in Computer Science, Engineering, Math, Business, or a technology-centric field AND a minimum of 8 years of relevant full-time work experience.
\\n - 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.
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Required Skills\\n
\\n - Demonstrated experience in hands-on data engineering, pipeline architecture, or data platform operations, including time spent leading and managing technical teams.
\\n - 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.
\\n - Proficient in SQL, Python, or Spark for large-scale data processing and data modeling.
\\n - Solid experience with ETL/ELT pipeline design, workflow orchestration (e.g., Composer/Airflow), CI/CD pipelines (Azure DevOps preferred), and Infrastructure-as-Code (Terraform).
\\n - Experience defining and managing operational metrics, SLAs, and KPIs for enterprise-level data integration platforms.
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Preferred Skills\\n
\\n - Familiarity with implementing data governance frameworks, metadata management, and data quality testing tools (e.g., Great Expectations, Dataplex).
\\n - Background in utilities, energy, or other regulated industries with exposure to NERC CIP compliance, security protocols, and data classification.
\\n - Experience supporting migration initiatives moving data workloads off legacy ecosystems (such as Azure, Databricks, and C3.ai) onto Google Cloud Platform.
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Vertex Solutions Inc. is an Equal Opportunity Employer and does not discriminate on the basis of race, color, creed, national origin, ancestry, religion, age, citizenship, sex, marital or veteran status, disability or handicap, sexual orientation or any other basis prohibited by applicable law.
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