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Experience leading Big Data engineering teams working in environments such as Hadoop and SparkExperience leading technology strategies with cloud Platforms including AWS, GCP or AzureExperience leading engineering teams working in streaming and stream integration or middleware platforms, tools, and architectures such as Kafka, Flink, JMS, or Kinesis.
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Experience with big data technologies such as Hadoop, Spark, or Kafka is a plus. As part of our continued growth and digital transformation efforts, we are seeking a skilled Data Engineer to join our team on a contract basis.
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Experience with big data technologies such as Hadoop, Spark, or Kafka. Demonstrated ability to translate complex data into actionable insights. Work closely with production and engineering teams to identify opportunities for process improvements and cost savings using data-driven insights.
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Applying knowledge and relevant work experience in Big data engineering (Hadoop, Spark, Scala, Kafka) and ETL pipeline development tools (tools: IICS/AWS Glue/Matillion/Abinitio SSIS/SnapLogic); preferable in P&C/L&A Insurance data warehouse.
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Experience with Big Data technologies (Hadoop, Hive Spark, Presto, Flink, Kafka). As a Data Engineer on the Insurance Tech Platform team, you’ll be a key contributor in crafting and building numerous internal tools and services to support insurance products, which help our communities handle risk with one-of-a-kind protection programs that are unmatched in the travel industry.
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Strong understanding of distributed systems, big data technologies (Hadoop, Spark, Kafka), and cloud platforms (AWS, GCP, Azure). Experience with data modeling, data warehousing, and data pipeline orchestration tools (e.g., Airflow, Composer.
Full-timeRemoteExpandApply NowActive JobUpdated 29 days ago - UpvoteDownvoteShare Job
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Insightful experience with big data technologies like Hadoop, real-time processing frameworks (e.g., Apache Spark), and advanced knowledge of SQL and data architecture. Hands-on experience working with technologies like Spark for data processing, ETLs for data pipelines and queueing systems (Kafka, RabbitMQ.
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MapReduce, Hive, HBase, NoSQL, Parquet, GLUE, Athena (queries), Kafka, Data Bricks, Snowflake, Tableau, Yarn, API, Java, C/C. 3-7+ years QA/QC Data Testing of Big Data.
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Implementation and tuning experience in the big data ecosystem such as Hadoop, Scala, Hive, Pig, Spark, HBase, HDFS, YARN, Sqoop, NiFi, Storm, Impala, Hawk, Flume, Kafka, Sqoop, etc.
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Experience with Big data architecture and pipeline, such as Hadoop, Hive, Spark, and Kafka is a plus. Experience in Data identification, querying, cleaning, wrangling, check quality, working with common relational and non- relational database in Big data environments on both on-prem and cloud such as Azure, AWS and Google cloud.
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Expertise in big data technologies (e.g., Hadoop, Spark, Kafka, and cloud platforms like AWS, Azure, and GCP). 5+ years of experience in data engineering principles and practices (e.g., big data technologies, data warehousing, data pipelines.
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Develop data ingestion programs to ingest real-time data from LIVE sources using Apache Kafka, Spark Streaming and related technologies. Job Title: Big Data Engineer.
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Experience with big data technologies and exposure to Hadoop, Spark, AWS Glue, AWS EMR etc. Built high throughput real-time and batch data processing pipelines using Spark, Kafka, on AWS environment with AWS services like S3, Kinesis, Lamdba, RDS, DynamoDB or Redshift.
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Familiarity with big data technologies such as Hadoop, Spark, or Kafka. Certifications related to cloud platforms (Azure, AWS) or data engineering (e.g., Microsoft Certified: Azure Data Engineer.
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Hadoop MapReduce, Cascading/Scalding, Apache Spark, AWS EMR. Spark streaming, Kafka, Apache Storm, Flink NoSQL. Hadoop MapReduce, Cascading/Scalding, Apache Spark, AWS EMR. Database/Search – Oracle, complex SQL queries, stored procedures, performance tuning concepts, SOLR, AWS RDS, AWS Aurora.
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