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Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake. Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake.
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Keep yarn from being damaged while transferring from yarn boxes to yarn pin trucks. Stack yarn on yarn pin trucks only one layer above top row of pins. Position Summary: The Creeler is primarily responsible for creeling assigned yarns on correct knitting machines in an efficient and safe manner.
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Must have experience with textile processing such as fiber extrusion, yarn formation, winding, warping, knitting, and heat treatment of fiber and textiles. Must have experience with textile processing such as fiber extrusion, yarn formation, winding, warping, knitting, and heat treatment of fiber and textiles.
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While turning yarn into an actual product, our Flatbed Knitting Machine Operators will interact with inspectors and other members of the Knitting Team to ensure that the correct product specifications are being met.
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The Production Operator is responsible for preparing and operating equipment in yarn preparation for the weaving process. As the Production Operator at Fenner Dunlop, you'll have to prepare and operate the equipment for yarn preparation.
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Five years of work experience in textile industry preferred (ex: Product design, yarn production, fabric manufacturing, polymer/fiber science, textile education, fashion merchandising) Five years of work experience in textile industry preferred (ex: Product design, yarn production, fabric manufacturing, polymer/fiber science, textile education, fashion merchandising.
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Big Data: Apache Spark, YARN, Livy, ZooKeeper, Ranger, Knox, Polybase. Big Data: Apache Spark, YARN, Livy, ZooKeeper, Ranger, Knox, Polybase. Data Sources: MongoDB, Cloudera, Teradata, Oracle, DB2.
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Manually positions yarn ends dropped from spin packs/chimney. Removes yarn wraps from rollers. Threads yarn position. Mohawk’s vertically integrated manufacturing and distribution processes provide competitive advantages in the production of carpet, rugs, ceramic tile, laminate, wood, stone and vinyl flooring.
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Containers: Docker, Kubernetes (k8), OpenShift, cloud storage, flannel. Familiarity with Linux script-based development environments and tools (Perl, Python, Ruby, PHP, etc. Machine Learning: SparkSQL, Java, Scala, Python, Jupyter.
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At Blueprint, you'll have the opportunity to work with multiple clients and teams, such as data science and product development, all while learning, growing, and developing new solutions. Industry experience with Microsoft Server products, network connectivity, UNIX or other operating systems, and database migration to the cloud or SQL Server version upgrades.
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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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The Creeler is responsible for maintaining proper yarn placement on the creel throughout the production of the carpet. The Creeler must possess the ability to evaluate yarn requirements relative to the job and in accordance with written specifications, and then plan creel maintenance accordingly.
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Extensive knowledge on yarn extrusion, yarn post processing, fabric manufacturing technology (weaving, knitting, braiding etc) Identify the state of the art equipment for yarn and fabric manufacturing, including post processing of textiles, and implementing tighter control of existing finishes.
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Experience with Big Data related component operation and maintenance, including Hadoop, Yarn, HBase, Hive, Spark, etc., is highly preferred. We are looking for a Senior Site Reliability Engineer - Big Data (Official Title: Senior Site Reliability Engineer I) for our San Diego, Cbased office hub.
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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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