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Ideally knowledge of GCP data services (BigQuery; Dataflow; Data Fusion; Dataproc; Cloud Composer; Pub/Sub; Google Cloud Storage) Google Associate Cloud Engineer or Data Engineer certification is a plus.
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One or more cloud certification such as: AWS Certified Solutions Architect - Associate, Microsoft Certified: Azure Solutions Architect Expert, Certified Cloud Security Professional (CCSP) or Google Professional Cloud Security Engineer.
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Web Services (AWS), VMC on AWS, Google Cloud Platform (GCP), Google Cloud VMware Engine (GCVE), VMware Horizon VDI, Citrix. Oversee the design, and workload management of Azure VMWare Services and migration of VMs to the cloud.
ExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) 2+ years of experience with AWS, GCP, Azure, or another cloud service. Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform, and services like AWS Glue, Azure Data Factory, or Google Dataflow. Expertise in data storage technologies such as relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), data warehouses (e.g., Snowflake, Redshift), and data lakes (e.g., Amazon S3, Azure Data Lake Storage.
ExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Establish strong software engineering principles for development in Python on the Azure/Google Cloud Platform. As a Lead ML/AI Engineer, you will drive the design and implementation of functionality related to the end-to-end ML/AI and Feature lifecycle management on Azure/Google Cloud Platform, leveraging and integrating the cloud native services with other standard operational and automation tools.
ExpandApply NowActive JobUpdated 19 days ago - UpvoteDownvoteShare Job
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Any experience with always-on and high-volume web server stack, Azure/GCP PaaS and Azure/Google networking, provisioning native Managed Apps & CI/CD pipelines. 4+ years of experience in the cloud SRE/DevOps/Infrastructure, or any related fields.
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3+ years' experience in a similar role supporting Cloud (specifically Microsoft Azure), DevOps, Advanced Server, and Windows PC Administration. Experience in project management, application design and integration, and cloud computing (specifically Microsoft Azure.
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Keywords: GCP, Google Cloud Platform, BigQuery, Composer, SQL, Python, Data Engineering, Data Pipelines. Six (6) plus years of experience in building data lakes and cloud data platforms.
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Experience with cloud-based systems such as AWS, Azure, or Google Cloud, including expertise in infrastructure-as-code tools such as Terraform or CloudFormation. Understand Cloud Native applications running on Kubernetes within AWS.
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Solid knowledge of data warehousing such as Hadoop, MapReduce, HIVE, Apache Spark, as well as cloud base data storage: Google Cloud Storage with various formats (Parquet, JSON, ORC, Avro, delimited.
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Experience with workflow orchestration management engines such as Airflow, Dagster, DBT, etcExperience with Cloud Services (AWS, Google Cloud, Microsoft Azure, etc). Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc.
$147,500 - $195,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Supporting the day-to-day IT Infrastructure Operation, primarily on Azure, but also support services in GCP & AWS - these technologies include Servers, Databases, Networks, Workstations, Patching, Google Workspace, Entra/AzureAD DS, Reporting and other IT systems and services as directed.
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We are seeking an experienced and highly skilled Senior Solutions Architect with elite caliber expertise in Google Cloud Platform (GCP) and Artificial Intelligence/Machine Learning (AI/ML.
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Position SummaryAs a Lead ML/AI Engineer, you will drive the design and implementation of functionality related to the end-to-end ML/AI and Feature lifecycle management on Azure/Google Cloud Platform, leveraging and integrating the cloud native services with other standard operational and automation tools.
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