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Someone with a deep understanding of big data technologies (e.g., Hadoop, Spark, Kafka) and experience in cloud platforms like AWS, Azure, or Google Cloud. Someone with a deep understanding of big data technologies (e.g., Hadoop, Spark, Kafka) and experience in cloud platforms like AWS, Azure, or Google Cloud.
ExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Strong experience with big data technologies such as Spark, Kafka, Kinesis, Airflow, Hadoop, Hive, Databricks, Snowflake etc. Strong experience with big data technologies such as Spark, Kafka, Kinesis, Airflow, Hadoop, Hive, Databricks, Snowflake etc.
ExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Design, Develop, Modernize/Migrate pipelines to DatabricksDevelop applications on Cloud preferably AWSExplore and build proof of concepts using open source NOSQL technologies such as HBase, DynamoDB, Cassandra and Distributed Stream Processing frameworks like Apache Spark, Kafka stream.
RemoteExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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5+ year support experience of Big Data technologies in Hadoop ecosystem - Hive, HDFS, MapReduce, Spark, Yarn, Kafka, Pig, HBase, Sqoop, Elastic Search, Kerberos. Implementation experience for Hadoop distribution platforms like Cloudera or AWS EMR.
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5+ years hands-on experience with the Big Data stack (HDFS, SPARK, MapReduce, Hadoop, Sqoop, Pig, Hive, Hbase, Flume, Kafka) 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 using modern technology ( e.g., Java, Cassandra, Kafka) and design patterns (microservices, APIs , streaming) Experience with Big Data Technologies ( i.e., Hadoop, HDFS , MapReduce, Hive, Pig, Spark, etc.
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Google Cloud Platform (Data Proc, BigQuery) Data storage and analysis toolsets, including modern distributed technologies such as SparkSQL, Hadoop, Presto, Hive, Kafka, Cassandra, Redshift or other equivalent solutions.
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Knowledge of big data technologies such as Hadoop, Spark, or Kafka. Certifications in AI or cloud technologies, such as AWS Certified Solutions Architect, Google Cloud Professional Data Engineer, etc.
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Experience with big data technologies such as Apache Kafka, Spark, Hadoop, or similar systems. Our primary tech stack consists of Golang, Kubernetes, Kafka, Elasticsearch, and GraphQL, but we also use tools like S3, ScyllaDB, Redis, SQS and many other AWS services.
ExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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3+ years of experience with distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) Utilize programming languages like Java, Python, SQL, Node, Go, and Scala, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka). Strong understanding of relational databases (e.g., Oracle, SQL Server, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra), and data integration technologies (e.g., ETL tools, data virtualization, API integrations.
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Expert knowledge and experience in Databricks, Lake House, Structured Streaming, Kafka, Delta Lake, Delta Live table, Delta share, etc. Data Engineer 4 will design and implement distributed data processing pipelines using Spark, Hive, Python, Airflow, and other tools and languages prevalent in the Hadoop ecosystem; You will be given the opportunity to own the design and implementation; You will collaborate with Product managers, Data Scientists, Engineering folks to accomplish your tasks.
ExpandApply NowActive JobUpdated 8 days ago - UpvoteDownvoteShare Job
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Ability to work with big data technologies (e.g., Hadoop, Spark, Kafka). Knowledge of infrastructure as code (IaC) tools like Ansible, Terraform, or CloudFormation. Knowledge of industry-specific requirements and compliance standards (e.g., GDPR, HIPAA, HITRUST) a plus.
Full-timeExpandApply NowActive JobUpdated 5 days ago - UpvoteDownvoteShare Job
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Big Data applications using PySpark or Scala-Spark on Hadoop, Hive and/or Kafka, HBase, MongoDB. Experience with Hadoop and big data projects; We navigate complex data issues in the fields of healthcare, defense, safety, urban planning, energy, cyber, land use, climate, disaster preparedness, disaster recovery, space, and social equity.
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Extensive experience working with RDBMS, Spark, Hadoop, Kafka. Experience with AWS services such as Athena, Glue, Lambda, S3, DynamoDB, NoSQL, Relational Database Service (RDS), Amazon EMR and Amazon Redshift.
Full-timeExpandApply NowActive JobUpdated 11 days ago
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As the job market remains highly competitive, we have seen a surge in "rage applying." This is when candidates apply to multiple jobs, often without considering whether they are truly interested in the role. Rage applying goes hand-in-hand with quiet quitting. Often, employees want to entertain the thoughts and feelings of leaving their job, but they aren't necessarily serious about leaving yet. Meanwhile, other employees engaging in this trend are actually trying to find a better role. As a recruiter, it can be hard to identify who are the real applicants in a sea full of quiet quitters, but understanding rage applying and identifying red flags will certainly help.
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Transparency in hiring refers to the open and honest communication and information sharing that takes place between employers and job candidates. It encompasses all aspects of the hiring process, from posting job descriptions to providing feedback on performance during and after the interview process. In today's job market, hiring transparency has become increasingly important for both employers and candidates alike.
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