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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Systems Manager, Google Cloud Platform Data Engineering, Enterprise Data & Analytics - **Company:** Vertex, Inc. - **Location:** New York, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Data Analysis, Automation of Tests, Microsoft Azure, Big Data, BigQuery, Cloud Storage, Data Transmissions, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Migration, Data Flow Control, Python (Programming Language), Machine Learning, Meta-Data Management, Standard Sql, Cloudera, Enterprise Data Management, Google Cloud, Data Classification, Apache Spark, Information Technology, Data Management, Terraform, Data Pipelines, Databricks - **Published:** August 14, 2026 - **Apply:** https://www.dice.com/job-detail/a73bce31-afe7-44a1-9470-c029c7801744 ## About the Role * Required: 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. * Required: Solid 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. * Preferred: 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 Google Cloud Platform., * Master''''s Degree in Computer Science, Engineering, Math, Business, or a technology-centric field with 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 with a minimum of 8 years of relevant full-time work experience. ## Description Seeking a 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, * Lead the platform tooling strategy, administration, and ongoing maintenance for the ED&A Data and AI platform team. * Design, implement, and manage comprehensive monitoring and observability solutions across all Google Cloud Platform platform services. * Develop and deliver consistent reporting on platform health, capacity, and usage metrics to stakeholders and leadership. * Establish and oversee the triaging process for platform issues, ensuring rapid identification and accurate routing to the right owners for resolution. * Enable, support, and maintain the platform''''s Model and Agent Garden ecosystem to support scalable ML and AI deployments. * Lead and develop the Platform Tooling team, setting priorities, managing day-to-day execution, and fostering a culture of continuous improvement. * Partner with data engineering, platform operations, and enterprise governance teams to align tooling capabilities with platform requirements and business use cases. * Define and report on operational metrics, SLAs, and KPIs for platform tooling and observability. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Making Data Warehouses fast. 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