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Demonstrate expertise and hands-on experience with Apache Spark tools such as Google Dataproc, Amazon EMR, and Databricks. 8+ years experience demonstrating an established track record in Data Engineering and Machine Learning; experience with GPUs as well as expertise in tuning and analyzing Apache Spark workloads.
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Experience with data processing technologies and tools, such as Spark, Hadoop(HDFS, Hive, MapReduce), Athena, Airflow, Luigi. Experience with cloud infrastructure services, such as AWS(S3, EMR, EC2, Glue, Athena, SQS, SNS) or GCP.
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Amazon Elastic MapReduce (EMR) is the industry-leading cloud big data platform for petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks such as Apache Spark, Apache Hive, Presto and HBase.
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Develop, deploy and maintain data processing pipelines using cloud technology such as AWS, Kubernetes, Lambda, Kafka, Airflow, Redshift, S3, Glue, and EMR. 10+ years of experience in data engineering on distributed frameworks such as Spark, Databricks, Kafka Streams, Hadoop etc.
$226,000 - $361,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. Experience in designing and developing ETL workflows using tools, including Apache Spark or AWS Glue.
$58,400 - $133,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Required Skills: Python, MWAA (Airflow), Spark Optimization, AWS Glue, AWS EC2, Apache Spark. · Develop test maintain and document data pipelines using DBT Spark AWS Glue and Airflow.
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Working knowledge of Spark on EMR is a preferred. Development languages/frameworks : Java/Scala, Apache Spark, Kafka, Vertica, JavaScript (React / Redux), MicroStrategy. Design, develop, and maintain high volume Java or Scala based data processing jobs using industry standard tools and frameworks in the Hadoop ecosystem, such as Spark, Kafka, Hive, Impala, Avro, Flume, Oozie, and Sqoop.
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Build modular set of data services using Python, SQL, AWS Glue, lambdas, API Gateway, Kafka, data build tool (dbt), Apache Spark on EMR among others. Knowledge of AWS technologies specifically MSK, EMR, Athena, Glue, lambdas, API Gateway as well as Python, SQL is a must.
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Amazon : EMR, Step Functions, SQS, LaMDA and AWS cloud-native architectures. Stream Processing : Kafka, Spark Streaming, Kinesis. We transform the way the world buys, sells, owns and uses cars, accelerating the industry with global powerhouse brands like Autotrader, Kelley Blue Book, Manheim and more.
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Experience of implementing Amazon EMR or Big Data technologies like Hadoop, Spark, Presto, Hive will be a plus. Working knowledge with primary AWS Services like S3, Lambda, Batch, Glue, Athena, EC2, EBS, CloudWatch, CloudTrail, ECS, ECR, EMR, IAM, SNS etc.
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Experience with streaming / data processing technologies such as Beam, Spark, Kafka, Airflow, HBase, Presto. Proven experience building enterprise-grade software in a cloud-native environment (GCP or AWS) using cloud services such as GCS/S3, Dataflow/Glue, Dataproc/EMR, Cloud Function/Lambda, BigQuery/Athena, BigTable/Dynamo.
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Experience with the following technologies: Distributed Processing (Spark, Hadoop, EMR), traditional RDBMS (MS SQL Server, Oracle, MySQL, PostgreSQL), MPP (AWS Redshift, Teradata), NoSQL (MongoDB, DynamoDB, Cassandra, Neo4J, Titan.
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You will help build these solutions utilizing cutting edge technologies (Spark, Python, EMR, AWS Glue, Lambda and Airflow) while ensuring our stacks are secure & well managed. Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, 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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4+ years experience with Distributed data/computing tools (Glue, Airflow, EMR, ECS Fargate, Kafka, Spark, Aurora, or MySQL) 4+ years experience with Distributed data/computing tools (Glue, Airflow, EMR, ECS Fargate, Kafka, Spark, Aurora, or MySQL.
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Experience with the Hadoop eco system, including HDFS, YARN, Hive, Pig, and batch-oriented and streaming distributed processing methods such as Spark, Kafka, or Storm. Experience with Amazon S3, Redshift, EMR and Scala.
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