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In-depth knowledge of big data technologies such as Hadoop, Spark, Kafka, and cloud platforms such as AWS, Azure, GCP, Snowflake, Databricks, etc. Provide technical expertise and guidance on the selection, and hands-on implementation, and optimization of big data platforms, tools, and technologies across multiple cloud environments (e.g., AWS, Azure, GCP, Snowflake, etc.
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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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Hadoop Admin Ops / SRE role supporting Platforms built around Big Data Technologies (Hadoop, Spark, Kafka, Impala, Hbase, Docker-Container, Ansible and many more). Hands-on with analyzing various Hadoop log files, compression, encoding, & file formats.
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R , Python ) • Data Integration, Data Security on Hadoop ecosystem. Experience with Big Data Analytics & Business Intelligence and Industry standard tools integrated with Hadoop ecosystem.
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Computer Science graduate ideally with specialization in Data Engineering or Machine LearningExperience in Hadoop technologies (Map/Reduce, Oozie, Pig, Hive, Spark, Kafka, HBase, Storm.
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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. As a big data engineer at Booz Allen, you ll implement data engineering activities on some of the most mission-driven projects in the industry.
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Good knowledge and Proven practical experience in migrating relational data base from on-prem to cloud OR to build cloud-based data lake using either Big Data / Azure Synapse / Databricks / Snowflake.
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Hands on experience in data engineering desired with extensive knowledge of SQL and PySpark, on Databricks Platform. Ability to design cloud and on premise data solutions and provide guidance across storage, big data platform services, Big Data ecosystem, vendor SaaS integrations, RDBMS and NoSQL databases is a plus.
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Develop and maintain data pipelines and work closely with data engineering team to migrate to production Oracle, Big Data platforms: Hadoop and Cloud data bases eg: Snowflake/Red Shift based on business need.
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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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Minimum of 5 years of hands-on skills in Python, Apache Spark, Hive, Cassandra, Snowflake, Elastic, Lucene, DataBricks, Hadoop, Redshift, Scala, Java, or other relevant technologies in data engineering.
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Hands-on experience with Spark or ScalaGood experience in end-to-end implementation of data warehouse and data martsPreferred Qualifications: Strong understanding of databases and big data software technologies, specifically Azure and Databricks.
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Hands-on experience with database systems (e.g. SQL, NoSQL, vector, search) and data warehousing solutions (e.g. Snowflake, Redshift, BigQuery) Strong understanding of data processing technologies and distributed computing frameworks (e.g. Hadoop, Hive, Airflow, Spark, DBT, Kafka, Flink.
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5+ years of experience with Distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. As a lead big data engineer at Booz Allen, you'll use your expertise to lead data engineering activities on some of the most mission-driven projects in the industry.
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Experience of implementing Amazon EMR or Big Data technologies like Hadoop, Spark, Presto, Hive will be a plus. Implementation experience of Big Data technologies like Hadoop, Spark, Presto, Hive, and Hue will be a major advantage.
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