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Strong hands-on experience with IBM Event Streams, Apache Kafka, IBM WebSphere MQ, Red Hat Ansible, and Red Hat OpenShift. Implement and maintain IBM Event Streams, Apache Kafka, IBM WebSphere MQ, Red Hat Ansible, and Red Hat OpenShift technologies.
ExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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Data: Good understanding of data & data processing tools (e.g. Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake) Spark, Kafka, SQL), of relational database technologies and of analytics databases (e.g. Redshift, Vertica, Snowflake.
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Stream Processing Master: Experience with batch and stream processing technologies like Apache Kafka and Spark to handle data pipelines of all sizes. Data Warehousing Guru: Familiarity with data warehousing solutions like ClickHouse, Redshift, BigQuery, or Snowflake for efficient data storage and retrieval.
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Experience with data warehouse and big data technologies such as Azure Synapse Analytics, Azure Data Lake Storage, Spark, Kafka, Databricks, and Containers. Implement data processing pipelines using Azure Data Factory, Databricks, Azure Machine Learning, Azure Logic Apps, Azure Function Apps, Azure Kubernetes container and other big data services.
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Deep understanding of cloud platforms (AWS, Google Cloud, Azure) and big data technologies (Apache Spark, Kafka). Deep understanding of cloud platforms (AWS, Google Cloud, Azure) and big data technologies (Apache Spark, Kafka.
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As an integral part of our team, you will not only need to tackle operational challenges at every layer of the system infrastructure, but also set up essential tools like Spark, Databricks, Snowflake, Kubernetes, and Kafka for data science infrastructure.
RemoteExpandApply NowActive JobUpdated 2 days ago
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