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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Advanced knowledge of cloud computing technologies such as Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Design and build data solutions using Databricks, SQL, Python, Spark, and Delta Lake in the Azure ecosystem (Blob Storage, Data Factory, Event Hubs). Establish CI/CD processes, test frameworks, infrastructure-as-code tools, and monitoring/alerting (Git, Terraform, Azure DevOps / GitHub Actions / Jenkins, Azure Monitor / Datadog.
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Experience with CI/CD delivery using code management, configuration management and automation tools such as GitHub, Azure DevOps, Jenkins, Maven, etc. Knowledgeable in multiple data management disciplines such as: Data governance, master data management, data integration, data modeling, SQL and relational database technologies, data warehousing, business intelligence, and analytics.
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Integration: Integrate Azure Databricks with other Azure services such as Azure Data Lake Storage, Azure Synapse Analytics, and Azure SQL Database, as well as third-party tools and data sources.
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Implementation: Lead the implementation of Azure Databricks solutions, ensuring best practices for performance, security, and data governance. Proficiency with Azure Databricks, Apache Spark, and data processing frameworks.
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Basic Job Functions The D365 Lead DevOps Engineer is responsible for leading the efforts in Microsoft Dynamics 365 Finance & Operations implementations, with a focus on environment management, release pipelines, ADO, automation, and data.
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Your team will manage the SDP data infrastructure, Terraform Cloud, Snowflake RBAC, and cost management for Snowflake and AWS. In addition to this, your team will also be tasked with developing Data Governance capabilities, specifically focusing on "Data Retention" and "Permission to Share.
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At least 2 years of hands-on experience with Azure, Databricks, and Hadoop distributed frameworks while handling large amounts of big data using Spark and Hadoop Ecosystems. Strong understanding of databases and big data software technologies, specifically Azure and Databricks.
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Expertise in DevOps, DevSecOps, and emergent experience with DataSecOps and Data Governance practices. Practical experience with Data Engineering and the accompanying DevOps & DataOps workflows.
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Knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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As the Data Engineering Intern , you will perform data engineering activities, using multiple tools and techniques, to ensure build out of a robust, resilient, and rationalized hybrid data ecosystem on Azure and Google Cloud platforms based on Ingredion’s analytics governance guidelines as well as industry best practices.
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Develop and enforce data governance policies, including data quality, lineage, and metadata management using tools like Azure Purview. Skills: Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, Azure SQL Database, Apache Spark, Hadoop, Databricks, and Azure HDInsight.
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Relevant Azure certifications such as Azure Solutions Architect Expert, Azure DevOps Engineer Expert, or Azure Administrator Associate. Champion security and governance practices in Azure cloud environments, implementing robust identity and access management, network security, and encryption measures.
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5 years of cloud development and data lake experience (prefer Microsoft Azure) including Azure EventHub, Azure Data Factory, Azure Databricks, Azure DevOps, Azure Blob Storage, Azure Data Lake, Azure Power Apps and Power BI.
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