Data Engineer, Operations and Infrastructure Data Science

Sunnyvale, CA, USA

Full-time

Onsite

$106K/yr - $151K/yr

Entry Level

Google is a leading technology company that provides innovative cloud solutions. As a Data Engineer, you will guide customers in managing their data on Google Cloud Platform, leading data migrations and building efficient data pipelines.

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Responsibilities

Design and manage the data pipelines that power Vertex AI operations, ensuring they are scalable and reliable
Build and refine automated agents to monitor system health and streamline operational workflows
Create intuitive dashboards that provide clear visibility into Vertex AI efficiency metrics for both technical and finance stakeholders
Partner closely with data science and finance teams to translate complex efficiency goals into actionable data models and tracking systems
Work with finance clients to ensure technical metrics align with budgetary and resource-planning requirements

Qualification

Required

Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience
1 year of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., MapReduce, Flume)
Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript
Experience managing client-facing projects, troubleshooting technical issues, and working with engineering and sales services teams

Preferred

Experience in technical consulting
Experience working with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments
Experience working with Big Data, information retrieval, data mining, or machine learning
Experience in building multi-tier high availability applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, TensorFlow)
Experience architecting, developing software, or internet scale production-grade Big Data solutions in virtualized environments