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Experience with the Azure platform: Azure Synapse Analytics, Azure Data Factory, Function Apps, Azure Databricks. Created Builds and Releases in Azure DevOps. Designed and developed ETL solutions using Azure Data Factory and SSIS.
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You will also understand Azure DevOps for structured development, including task management, and managed code deployment (including CI/CD pipelines). Data Movement (Apache Spark, plus Azure Data Factory and legacy SSIS.
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The infrastructure is running on multi-cloud and hybrid-cloud environments (AWS, Microsoft Azure, Oracle Cloud just to name a few, and Bare Metal servers), and is administered completely programmatically, via the company's Stack with integrations to Nomad and Terraform.
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Strong experience with AWS, Azure, or GCP platform capabilities and services (Cloud Architect, Cloud DevOps Engineer, or Cloud Security Engineer). Serve as the cloud SME to clients within design, architecture, and deployment engagements in the AWS, Azure, or GCP space using automated orchestration and configuration management techniques.
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Extensive experience that demonstrates a strong knowledge and skill of information security technology focusing on IAM Applications (e.g., ForgeRock, SailPoint, RSA, Daon, Ping, Azure, SiteMinder, etc.
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Technical implementation knowledge in Azure Synapse, Azure Data Factory, Azure Data Catalog/Purview, Azure Analysis Service, SQL Datawarehouse, Kusto, Data Analysis, Azure Data lake, Azure Databricks, Power BI.
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Title: Azure Engineer with (AI/Client exp) Define standards and create reference implementations pattern for machine learning / AI solution and Azure PaaS. Qualifications Knowledge of Azure relevant certifications such as Azure Solutions Architect, Azure AI Engineer.
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CI/CD concepts and tools (Azure DevOps, Git, GitLab, Gradle, Nexus or similar tools) - Deployment pipelines, automated build, and configuration tools. Experience with cloud environments (e.g. AWS, Azure, Openstack, VMWare, or GCP.
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Experience working with Cloud Service Providers, including AWS, Microsoft Azure, and Google Cloud Platforms. Act as a subject matter expert for end-to-end cloud systems engineering including identifying cloud providers, networking, provisioning, and management/governance.
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Building Angular apps using Azure DevOps. Azure DevOps build and release pipelines for deploying code and infrastructure as code. Building.net Core apps using Azure DevOps. Building Aurelia apps using Azure DevOps.
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Monitoring, debugging, and troubleshooting Azure, GCP, and AWS cloud managed services, including Spark, EMR, DataProc, Composer (Airflow). Experience in building highly performant and scalable data ingestion pipelines, and hands-on experience with cloud-based big data technologies such as Spark, BigQuery, and Azure Data Lake Analytics.
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Knowledge/experience of cloud technologies such as AWS S3 and Relational Database Service (RDS), Microsoft 365, Azure, Google, Salesforce, Slack, Box, Okta, GitHub, Jira, and Zoom. This includes Microsoft 365, Azure, Google, AWS (S3 and RDS), Salesforce, Slack, Box, Okta, GitHub, Jira, and Zoom. An ideal fit for this role will have a consulting-first mindset, ability to recognize customer needs and pain points, demonstrate strong communication skills (written, verbal, presentation), and promote customer satisfaction at the highest level.
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Hands-on with Azure Repos and Azure DevOps and integration of Azure DevOps to AWS via Terraform. Experience on Ruby / Ansible / AWS / Azure / GCP / Terraform / Docker. Hands-on with Azure Repos and Azure DevOps and integration of Azure DevOps to AWS via Terraform.
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Cloud Platforms: AWS (including Bedrock), Google Cloud, or Azure. Frameworks: PyTorch or TensorFlow, FastAPI or Flask for API development. The ideal candidate will have a strong background in AI and NLP, with hands-on experience in building and optimizing LLM-based systems.
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The candidate needs to be an advanced practitioner of SQL, R, Python and be familiar with working with data in an Azure Cloud environment using DataBricks and other cloud analytics tools. 3 years of specific data analysis tools: Python, R, SAS, PySpark, Tableau Server, PostgreSQL, SQL, Django, ArcGIS.
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azure job Title: engineering manager in Hackensack, NJ
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