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Experience with distributed computing systems and/or cloud infrastructures (e.g. Spark, Hadoop, YARN, Kubernetes, AWS, etc.) Experience coding with Java, Go and/or web technologies (e.g. HTML, CSS, JavaScript, Python/Ruby, Django/Flask/Ruby on Rails, etc.
$82,000 - $140,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with the Hadoop eco system, including HDFS, YARN, Hive, Pig, and batch-oriented and streaming distributed processing methods such as Spark, Kafka, or Storm. Experience with the Hadoop eco system, including HDFS, YARN, Hive, Pig, and batch-oriented and streaming distributed processing methods such as Spark, Kafka, or Storm.
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Extensive knowledge on Hadoop eco-system such as HDFS, Yarn, HIVE and SPARK. Perform Administration and Engineering activities on Open-source Hadoop, Open-source Spark, Airflow, Machine learning platform running on Open-source Kubernetes clusters.
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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. Implementation experience for Hadoop distribution platforms like Cloudera or AWS EMR.
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Troubleshooting the Hadoop Yarn jobs is must. 6-8 years of strong experience in Spark, Python, Shell Scripting, PostgreSQL, Hadoop, AWS (S3, EC2, SNS, SQS, Lambda, ECS, Glue, IAM, and CloudWatch), Databricks (Delta lake, Notebooks, Pipelines, cluster management, Azure/AWS integration.
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Hadoop (or similar) Ecosystem (MapReduce, Yarn, HDFS, Hive, Spark, Presto, Pig, HBase) Experience with traditional relational databases, NoSQL databases and/or distributed systems such as Hadoop/Hive, BigQuery, Redshift.
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Implement Hadoop job orchestration using Shell scripting, and Airflow. Must have work experience on Hadoop as data warehouse/Data Lake implementations. Should be able to demonstrate work experience in Hadoop, Spark, hive.
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Weiteres zum (technischen) Umfeld: Zookeeper, Elasticsearch (Logstash, Kibana, Beats und X-Pack), MapReduce, NiFi, Zeppelin, Hortonworks Data Platform, Falcon, Atlas, Sqoop, Flume, Kafka, YARN, Pig, Hive, HBase, Accumulo, Phoenix, Storm, Flink, Ambari, Ranger, Knox, Oozie.
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Family planning support, including fertility, adoption, and surrogacy assistance. Many teams do offer hybrid options (WFH a day or two a week), allowing our employees to strike the right trade-off for their personal productivity.
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Use the following big data and cloud technologies: Hadoop, MapReduce, Hive, HBase, Sqoop, Spark, Flume, YARN, Kafka, Azure, AWS, Cassandra, Mongo DB, and REST API. Use data integration products, including Informatica Power Center Big Data Edition (BDE) and Azure Data Factory.
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Technical Environment: Python and PySpark programming; Hadoop; HDFS, map-reduce, YARN, AWS EMR; Hive, HBase, parquet, ORC, Spark SQL, Sqoop, Apache Hudi; orchestrating ETL pipelines involving data sourcing, transformations and publishing using Apache Airflow; Apache Kafka; Advanced SQL for data profiling and data validation; Unix commands and scripting; JIRA, Gitlab, and Subversion; EMR; S3; RDS; MWAA; Jenkins; CircleCI; Agile; AWS.
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Good knowledge in Querying and analyzing large amount of data on Hadoop HDFS using Hive and Spark Streaming and working on systems like HDFS, YARN, Hive, HBase. Performance tuning and troubleshooting of various Hadoop components and other data analytics tools in the environment: HDFS, YARN, Hive, HBase.
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Hands-on experience in Scala/Big Data technologies (Spark/Flink, Hive, Oozie, Hbase/Cassandra/MongoDB, Redis, YARN, Kafka) Experience with Java/Scala programming language Experience with shell scripting Experience with ETL job design using Nifi/Streamsets/Talend/Pentaho/etc.
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Experience with distributed computing technologies such as Hadoop MapReduce, Scala/Spark-SQL, YARN/MR2 Expertise in designing, implementing and supporting highly scalable data systems and services in Scala Expertise building and running large-scale data pipelines, including distributed messaging such as Kafka, data ingest to/from multiple sources to feed batch and near-realtime/streaming compute components.
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Excellent knowledge on Hadoop Components such as HDFS, Name Node, Data Node, YARN, Resource Manager, Node Manager, and Map Reduce programming paradigm. Hands-on experience on Apache Hadoop ecosystem components like MapReduce, Sqoop, Pig, Hive, HBase, Spark, Kafka, Oozie, Flume, Zookeeper.
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