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Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud Platform) and containerization technologies (e.g., Docker, Kubernetes), code management tools and frameworks (e.g., Ci/CD, Git) for understanding and managing big data solutions.
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Deep understanding of distributed computing principles, data management concepts, and big data ecosystem components (e.g., Hadoop, Spark, Kafka, Apache Delta Lake). Technical Architect-Big Data.
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Big Data Expertise : 3+ years of hands-on experience with major Big Data technologies and frameworks, including Spark, Hive, ZooKeeper, HDFS, Presto, Hadoop, MapReduce, Tensorflow, and more.
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Big Data Technology Proficiency: Engage with a range of Big Data technologies, including but not limited to Spark, Presto, Hadoop/HDFS, Hive, and more. Hands-On Customer Engagement: Work directly with customers on intricate deployments, leveraging your expertise in Big Data both on-premise and in cloud environments to maximize the potential of the Alluxio platform.
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Experience with the Big Data stack (HDFS, SPARK, MapReduce, Hadoop, Sqoop, Pig, Hive, Hbase, Flume, Kafka) as well as with the No-SQL (e.g. MongoDB, HBase, Cassandra) As a Data Engineering & Analytics practice lead you will be responsible for growth, business development and providing advisory and thought leadership on the provision of analytics environments leveraging Cloud based platforms, big data technologies, including integration with existing data and analytics platforms and tools.
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Specifically, this experience must be in writing Big Data data engineering jobs for large scale data integration in AWS. Prior experience in writing Machine Learning data pipeline using Spark programming language is an added advantage.
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Data Warehouse and Big Data Experience: Possess hands-on experience in designing and maintaining data warehouses and/or data lakes using big data technologies such as Hadoop, Spark, and especially DataBricks.
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Expertise in Big data technologies such as Spark, Hadoop, Databricks, Snowflake, EMR, Glue. Through hands-on engineering approach in Databricks environment, this individual will deliver data engineering toolchains, platform capabilities and reusable patterns.
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Job Schedulers – Airflow, Oozie or other ETL Scheduler Data Analytics – AWS Cloud Native Services Big data architecture – Python, Scala, Hadoop Bigdata, Hive, Pyspark etc. Data bases (SQL, NoSQL) and Data Platforms (Databricks, Snowflake) Strong skills with Database management, Data modeling and design DevOps and Security CI/CD, Serverless compute, OAuth, SSO, MFA etc.
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These technologies may include artificial intelligence, machine learning, statistical techniques, mixed integer linear programs, reporting/big data analysis, dashboarding, data integration, and other heuristics/algorithms.
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Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark). Experience delivering advance analytics solutions, reporting, and managing big data.
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Design and develop scalable data warehousing solutions, building ETL pipelines in Big Data environments (cloud, on-prem, hybrid) Familiarity with data pipelines and orchestration frameworks (Spark, Hadoop, Databricks, Airflow.
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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 cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies (e.g., Hadoop, Spark) is a plus. Relevant certifications (e.g., Certified Data Management Professional, AWS Certified Solutions Architect) are preferred.
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Strengthen the Data engineering team with Big Data solutions running on Azure. Strengthen the Data engineering team with Big Data solutions. Establish DevOps processes for marshalling big data work products from development to production.
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big data jobs Title: sonographer in Bentonville, Kansas
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