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Dot submit Big Data Kafka profiles, get Kafka with Java background. Provide expertise and hands on experience working on Kafka connectors such as MQ connectors, Elastic Search connectors, JDBC connectors, File stream connector, JMS source connectors, Tasks, Workers, converters, Transforms.
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Cloud Platforms: Experience with cloud platforms like AWS, Azure, or Google Cloud Platform, and their big data services (e.g., Amazon EMR, Azure HDInsight, Google Cloud Dataproc). Streaming Data: Knowledge of stream processing frameworks like Apache Kafka or Apache Pulsar is valuable for handling real-time data streams.
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Hands on experience architecting and delivering solutions using the Azure Data Analytics platform and solutions including but not limited to Azure Databricks, Azure Cosmos DB, Azure Data Factory, Azure Logic Apps, Azure Functions, Azure Storage, Azure SQL Data Warehouse, Azure Data Lake, Azure Search, and Snowflake.
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Work on Search algorithm, web services, search indexing, data mining, big data, Hadoop, deep learning, recommendations, and performance by developing a Search platform at Adobe that would power Adobe Clouds such as Creative Cloud, Document Cloud and Marketing Cloud.
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Experience with Big data technologies to include Data Ingest (JSON, Kafka, Microservices, Elastic Search), Analytics (HIVE, SPARK, R, PIG, OOZIE workflows), Elasticsearch, Hadoop (HIVE data, OOZIE, Spark, PIG, IMPALA, HUE), COTS Integration (Knowi, MongoDB, Oracle, MySQL RDS, Elastic, Logstash, Kibana, Zookeeper, Consul, HADOOP/HDFS), Docker and Chef.
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5+ year support experience of Big Data technologies in Hadoop ecosystem Hive, HDFS, MapReduce, Spark, Yarn, Kafka, Pig, HBase, Sqoop, Elastic Search, Kerberos. Extensive experience in designing, engineering and managing data lake ingestion, validation, transformation and consumption services leveraging cloud data tools like Hive, Spark, EMR, Glue ETL and Catalog, Snowflake etc.
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Experience using modern containerization software including Docker, OpenShift and Kubernetes Experience with Snowflake, Microsoft Power BI, Splunk, Securonix, and CywareProficiency with automation tools such as Ansible, Chef or Puppet Project Management experience Industry related certifications such as PMP, GSLC, GISF, GSEC, CISSP Experience with big data tools: Hadoop, Spark, Kafka, etc.
$128,900 - $195,400 a yearFull-timeRemoteExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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10+ years direct experience in Data Engineering with experience in tools such as: Big data tools: Hadoop, Spark, Kafka, etc. Experience with message queuing, stream processing, and highly scalable big data data stores.
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Experience with big data frameworks and tools, such as Spark, Hadoop, Kafka, Hive, and Airflow. Experience building production systems with more modern ETL and data systems, such as AWS Glue, Databricks, Snowflake, Elastic, and Azure Cognitive Search.
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Development 1+ years of Experience with AWS/GCP Cloud or equivalent on data integration with Glue, Kafka, Elastic Search, Lambda, S3, Redshift, RDS, Kubernetes, or BigQuery ecosystems.
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Hands-on Experience in Big Data and cloud technologies tools, i.e. Hadoop, Hive, Iceberg, Kafka, Spark, Presto, Airflow, Elastic Search, Docker and Kubernetes. Design and build data architecture for data science projects using big data technologies.
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Hands-on Experience in Big Data tools, using Hadoop, Kafka, Spark, Presto, Airflow, Elastic Search, Docker and Kubernetes. Plumes technology stack is among the deepest in silicon valley, covering embedded firmware, mass data pipeline and processing, big data analytics, SDN controller framework, customer API integrations, WebUI management interfaces and consumer facing mobile apps.
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Containerization and Orchestration: Familiarity with containerization (Docker) and orchestration tools (Kubernetes) for deploying and managing big data applications. Optimization: Proficiency in optimizing big data applications for performance, throughput, and resource utilization.
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Experience working with big data systems (Spark, EMR, Kafka, S3, Airflow) and programming languages (Java, Scala, or Python). Good understanding of system architecture, including experience with big data technologies, streaming architecture, and data pipelines.
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Experienced in developing large scale enterprise applications using Big Data open-source solutions such as Hadoop, Spark, Kafka, and Elastic Search. Designing and developing distributed applications using Big Data technologies such as Hadoop, Spark, Kafka, and Elastic Search.
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