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Should have certified in Spark from Cloudera or Databricks or Hortonworks. Knowledge in Kafka, Pig, MapReduce, HBase, Sqoop, Solr is a plus Exposure in BI Tools like Tableau, MicroStrategy etc.
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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.
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Strong programming skills in languages such as Python, Java, or Scala, with experience in data processing frameworks like Apache Spark or Apache Flink. Data Processing Frameworks: Lead the implementation and optimization of data processing frameworks and technologies, such as Apache Hadoop, Apache Spark, and Apache Flink, to enable efficient data processing and analysis.
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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch.
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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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3+ years of Experience with Apache Spark, Kafka, Pig, Oozie, Flume, and Streaming and similar distributed computing technologies. Strong mathematical background with strong knowledge in at least one of the following fields: statistics, data mining, statistics, operations research, econometrics, and/or information retrieval (image processing (OpenCV) and natural language processing is a plus.
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Our portfolio includes Tinder, Match, Hinge, Plenty of Fish, The League, and others, each designed to spark meaningful connections for singles worldwide. Our portfolio includes Tinder, Match, Hinge, Plenty of Fish, The League, and others, each designed to spark meaningful connections for singles worldwide.
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Including data processing using Apache Spark, Delta Lake, Unity Catalog, and MLflow. Builds and manages data warehouses using technologies like Amazon Redshift, Google BigQuery, or Snowflake. including data processing using Apache Spark, Delta Lake, Unity Catalog, and MLflow.
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Experience with large scale streaming platforms (e.g. Kafka, Event Hub, Databricks Live Table / Structured Streaming), processing frameworks (e.g. Spark, Databricks) and storage engines - stream analytics.
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Experience with one or more SQL-on-Hadoop technology (Hive, Impala, Spark SQL, Presto) Experience with one or more relevant tools (Sqoop, Flume, Kafka, Oozie, Hue, Zookeeper, HCatalog, Solr, Avro.
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Domain knowledge in Trust & Safety, Identity, Fraud Detection and PreventionExperience supporting business-critical systems for multinational customers at scale in a B2B environment. Match Group is headquartered in Dallas, TX, with offices in LA and New York. In this role you will: Lead product discovery, prioritization and execution of new features, enhancements, and optimizations that maximize the performance of our suite of bad actor detection services and APIs. Clearly communicate the vision and roadmap to senior management and key stakeholders; ensuring alignment with the company-wide trust & safety objectives.
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You have experience with Cloud technologies such as GCP, Azure as well as with Spark, Hive and TensorFlow etc. The work includes solving problems such as Ambient and Perishable inbound network design, middle mile network flow optimization, last mile delivery and returns design, inventory placement and targets, transportation and monetization strategies to provide Walmart a competitive edge in the medium to long term.
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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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Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc. Experience with high speed distributed computing frameworks such as AWS EMR, Hadoop, HDFS, S3, MapReduce, Apache Spark, Apache Hive, Kafka Streams, Apache Flink etc.
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Experience with Spectrum Conductor or Databricks with Apache Spark. Strong understanding of core Hadoop services such as HDFS, MapReduce, Kafka, Spark and Spark-Streaming, Hive, Impala, HBase, Kudu, Sqoop, and Oozie.
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