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Preferred Qualifications Experience with distributed computing technologies including Hadoop, HBase, Cassandra, Elasticsearch and Apache Spark a plus. Hands-on experience working with Elastic Search, Mongo DB, Node, Hadoop, Map Reduce, Spark, Rabbit MQ, NiFi and Docker.
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Ideally you have an in-depth understanding of streaming data technologies like Amazon Kinesis or Apache Kafka, and experience with open-source data processing frameworks like Apache Spark, Apache Flink, or Apache Storm.
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Expertise in Cloudera Big Data Technologies, including Cloudera Distribution of Hadoop (CDP), Apache Hadoop, Apache Spark, Apache Hive, Apache Impala, and Apache HBase, with hands-on experience in deploying, configuring, and optimizing these technologies for large-scale data processing and analytics.
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Familiarity with ElasticSearch and other big data technologies like MapReduce, Apache Spark, and Scala. Familiarity with container technologies (Docker, ECR, ECS, EKS), Infrastructure as Code (IaC) tools like Terraform, and Elasticsearch is a plus.
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This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform.
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Big Data Technologies: Experience with Elasticsearch, Logstash, Kibana, Kafka, ksql, NiFi, Apache Spark, ServiceNow. Elastic Engineer Certification: Certified Elastic Engineer with experience developing logstash and ingest pipelines.
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Hands-on experience with the Databricks platform and Apache Spark. Proficient in programming languages such as Python, Scala, or SQL. In-depth knowledge of Apache Spark and its ecosystem.
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Design, implement, and maintain complex ETL pipelines using tools such as Apache Spark, Apache Airflow, and AWS Glue for both batch and near real-time data processing. Expert knowledge of distributed data processing frameworks like Apache Spark, Hadoop, or Flink.
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DataBricks Architect with Apache, Spark, Python, ETL. Hands-on experience with Databricks platform and Apache Spark. In-depth knowledge of Apache Spark and its ecosystem.
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O Programming Language: Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting. Use programming languages including but not limited to Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting.
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Experience in Installing, Configuring, tuning, and monitoring Apache Spark and Airflow on Kubernetes. Strong knowledge of Apache Spark and Apache Airflow architecture.
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4+ Experience with technologies Apache Mesos, Marathon, or Apache Spark. 2+ Experience with distributed database systems (Cassandra, ElasticSearch) Utilize programming languages like Java, Python, SQL, Ruby and Go, Container Orchestration services including Docker and Kubernetes, CM tools including Ansible and Terraform, and a variety of AWS tools and services.
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At least 4 years of experience in data engineering working with Big Data Technologies: Apache Spark, Pyspark, Hadoop, and DataBricks with delta lake. Experience with Apache Airflow, Hive, Snowflake, Kafka, Python.
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Leverage AWS cloud services like S3, Kinesis, Redshift, EMR, Glue, Lambda, and others, in combination with Data Lakehouse platform/Apache Spark Integration for advanced data processing and analytics.
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AWS re:Invent 2020: How New Relic is migrating its Apache Kafka cluster to Amazon MSK. Software Development Engineer, Managed Streaming for Apache Kafka Come build the future of data streaming with the Amazon Managed Streaming for Kafka (MSK) team.
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