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You may possess a vast background (additional certificates help support your deep security knowledge (CISSP, GSEC, CISA, CISM, CRISC, MCSD), comprising of functional experience working in a global hybrid cloud environment, including AWS, Azure, O365, and GCP. Experience with Data Governance or Data Compliance Standards is valuable.
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Experience in one or more of the following technologies: Azure AD, ADFS, SSO, multi-factor authentication, SAML, IGA, or Privileged Access Management. Create and conduct training for staff on the use of IGA related tools and processes.
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Microsoft Azure Cloud Engineer Job Responsibilities:-Focus on optimizing existing systems, building infrastructure, and eliminating work through automation. -In-depth knowledge of security and IAM/RBAC within Azure, including the management and operation of Azure ASG, Azure Key Vault, Azure NSG, Azure AD, Azure Security Center, Azure Policy, and Azure Management Group/Subscriptions.
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Demonstrating knowledge of machine learning and artificial intelligence workloads and considerations on Azure; Working with Agile development methodologies and product tracking solutions such as Version Control Systems (e.g. Github), Issue Management Systems (e.g. JIRA, Azure DevOps etc.
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Hands-on experience with cloud data platforms such as Azure Synapse Analytics, Azure Databricks, or Microsoft Fabric. Design scalable and efficient data pipelines using Azure Synapse, Databricks, or other relevant Microsoft Fabric technologies.
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3+ most recent years proven experience as an Infrastructure Operations Engineer or similar role, with a focus on Azure-based infrastructure setup and management. Hands-on experience with Infrastructure as Code (IaC) tools such as Terraform.
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The right candidate will have had hands-on experience with high end IGA tools, such as Saviynt, as well as Active Directory, Access Management solutions, Identity Providers (OKTA and/or Azure AD SSO) in order to integrate and manage the overall Identity process.
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Hands-on experience and proficiency in Data Governance tools likeInformatica IDMC, Collibra, and Alation across all DG capabilities, aka, Data Catalog, Metadata Management, Glossary, Lineage, Data classification / Access / Security, Data Marketplace.
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In-depth knowledge of Data Engineering concepts building platforms at Petabyte scale preferably on Databricks, Azure, Snowflake or similar. Strong expertise in enterprise integration technologies such as ESBs, iPaaS, and middleware platforms such as SAP BTP IS, Azure Integration Services, Solace, MuleSoft or similar.
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Knowledge of public clouds (AWS, Azure, GCP) with familiarity in data warehousing solutions like Snowflake and Databricks. You know that success hinges on commitment, that our differences make us stronger, and that the finish line is always sweeter when the whole team crosses together.
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As one of Fortune ® magazine's "World's Most Admired Companies™" 9 of the last 10 years, one of Fast Company’s Most Innovative Companies, and a top scorer on Bloomberg’s Gender-Equality Index, we are committed to innovation and excellence.
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Manage the deployment and operationalization of AI models on cloud platforms (e.g., AWS, Azure, Google Cloud) and oversee the end-to-end MLOps lifecycle. Architect AI systems with a focus on data security, privacy, and compliance with industry standards, including those specific to medical devices.
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Certifications related to cloud platforms (Azure, AWS) or data engineering (e.g., Microsoft Certified: Azure Data Engineer). Extensive hands-on experience with Snowflake or similar cloud-based data warehousing platforms.
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Familiarity with cloud platforms (, AWS, Azure, GCP) and their data services (, S, Redshift, BigQuery). Hands-on experience with big data technologies and frameworks such as Hadoop, Spark, Kafka, and NoSQL databases.
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And Micro-frontend architecture Knowledge on AWS/ Azure / Jenkins / Other build tools Good to have a knowledge of DevOps and CI/CD. A delivery focused approach to work and the ability to work without direction.
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