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Hands-on experience with big data technologies (such as Hadoop, Spark, Kafka). Implement and optimize data solutions in enterprise data warehouses and big data repositories, focusing primarily on movement to the cloud.
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Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage. Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
$160,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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5+ years hands-on experience with the Big Data stack (HDFS, SPARK, MapReduce, Hadoop, Sqoop, Pig, Hive, Hbase, Flume, Kafka) Hands-on experience with "productionalizing" Big Data applications (e.g. administration, configuration management, monitoring, debugging, and performance tuning.
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Expertise with SQL, NoSQL, Python, R, Javascript programming languages and big data environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker, Kubernetes etc.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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Create information solutions covering data security, data privacy, metadata management, multi-tenancy and mixed workload management across Hadoop and NoSQL platforms, spanning on-premise and Cloud based deployments.
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Experience in developing partner led sales in collaboration with data platform and tool vendorsIn-depth knowledge of big data technologies such as Hadoop, Spark, Kafka, and cloud platforms such as AWS, Azure, GCP, Snowflake, Databricks, etc.
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Hands-on experience working with technologies like Spark for data processing, ETLs for data pipelines and queueing systems (Kafka, RabbitMQ) Insightful experience with big data technologies like Hadoop, real-time processing frameworks (e.g., Apache Spark), and advanced knowledge of SQL and data architecture.
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For example, reconciliation using Big Data technologies, growing and optimizing customer payments options, tax calculations in milli-second response times and a mesh of supplier payment options like virtual credit cards.
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Proven ability to handle and process large volumes of data and ability to utilize big data frameworks and tools such as Apache Spark, Hadoop or Kafka for real-time data ingestion and processing.
$230,000 a yearFull-timeExpandApply NowActive JobUpdated 14 days ago - UpvoteDownvoteShare Job
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Experience with big data technologies such as Apache Hadoop, Spark, Kafka, and others. The ideal candidate will have a robust background in big data analytics, data engineering, and cloud computing, with a particular focus on building and optimizing data lakes on AWS. This role is crucial for designing, implementing, and managing advanced data solutions using AWS and Data Lakehouse platforms.
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Familiar with multiple Big Data technology stacks (e.g. Hadoop, Yarn, HDFS, MapReduce, Hive, Spark, Kafka, etc) Aware of various tools, techniques, and hands-on knowledge on some of the tools, techniques (e.g. Python, Scala; data mining, predictive analytics.
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Java and Python)Hands-on experience with SQL database designGreat numerical and analytical skillsDegree in Computer Science, IT, or similar field; a Master’s is a plusData engineering certification ( IBM Certified Data Engineer) is a plusExperience with big data tools: Hadoop, Spark, Kafka, etc.
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Experience Proven experience (5+ years) as a Senior Database Administrator, with a focus on Data Warehouse and Data Lake environments Technical Skills In-depth knowledge of database management systems such as Oracle, SQL Server, MySQL, or PostgreSQL. - Hands-on experience with big data technologies like Hadoop, Spark, and related ecosystems (e.g., Hive, HBase, Kafka.
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3 plus years of experience in building use cases / solutions especially around AI/ML cognitive services, based on Cloud infrastructure and services such as Azure cloud platforms and On- premise environmentsExpertise with SQL, noSQL, Python, R, Javascript programming languages and big data environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker, Kubernetes etc.
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