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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. Experience with a public cloud, including AWS, Microsoft Azure, or Google Cloud.
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At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) 2+ years of experience with OpenSearch, Kafka, Vue.js and some exposure to Machine Learning.
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Integrate Vertex AI with other Google Cloud services such as BigQuery, Cloud Storage, and Dataflow. Google Cloud Professional Machine Learning Engineer certification.
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Knowledge and proven use of contemporary data mining, cloud computing, and data management tools: Microsoft Azure, AWS Cloud, Google Cloud, Hadoop, HDFS, MapR, and Spark.
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4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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At least 4 years of experience in data engineering working with Big Data Technologies: Apache Spark, Pyspark, Hadoop, and DataBricks with delta lake. Experience with Apache Airflow, Hive, Snowflake, Kafka, Python.
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Experience with big data technologies such as Apache Hadoop, Spark, Kafka, and others. Leverage AWS cloud services like S3, Kinesis, Redshift, EMR, Glue, Lambda, and others, in combination with Data Lakehouse platform/Apache Spark Integration for advanced data processing and analytics.
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3+ years of experience with AWS, GCP, Microsoft Azure, or another cloud service. Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment.
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Skills: Google Cloud Platform - Professional Data Engineer, Google BigQuery, Hadoop Big Data Stack, UNIX Shell Scripting, Agile Methodology. Create and manage data storage solutions using Google Cloud Platform services such as BigQuery, Cloud Storage, and Cloud SQL.
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Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. Lead Data Engineer (Python, AWS, Kafka.
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Support cloud functions for Containerization, Changed Data Capture, and API Management (e.g. EKS, Rancher, Docker, ArgoCD, Grafana, ECR, and Kafka). Minimum Qualifications: A minimum of five (5) years of concurrent experience with architecting IT solutions using DevOps methodologies in multi-cloud environments, inclusive of Amazon Web Services, Google Cloud Platform and Azure cloud environments.
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2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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Hands-on knowledge of Cloud and Distributed Data Storage, including expertise in HDFS and ADLS. Strong understanding of data integration technologies, including Spark, Kafka, eventing/streaming, and Azure DataFactory.
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Implement secure integrations using Google security and networking technologies (IAP, VPC, and PSC) Experienced with building and maintaining distributed and stateful applications like Apache Kafka, Elasticsearch, Redis on the Kubernetes or VM clusters.
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Experience with cloud computing platforms (, AWS, Azure, GCP) and cloud native computingProficiency in programming languages like Python, Java, Scala, C#, SQLExpertise in big data technologies such as Hadoop, Spark, Kafka, and NoSQL databasesKnowledge of data modeling, ETL processes, and data warehousing conceptsExceptional problem-solving abilities and teamwork skills.
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