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Experience working with cloud technologies (Docker, Kubernetes); Azure / AWS / GCP / OpenShift infrastructure experience. Job Description: As a Data Analytics Engineer, you will: Evolve Extract, Transform, Load (ETL) pipelines of structured and unstructured data from a verity of disparate sources to meet the needs of an advanced Insider Threat program.
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Preferred Qualifications Understanding of, and/or hands-on experience with, Cloud technologies, such as AWS, Microsoft Azure, etc. They will have a strong desire and capability for problem solving, data analysis and troubleshooting, analytical thinking, and experimentation, with a particular interest or aptitude in the Cloud arena.
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Experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying machine learning models at scale. We are the "controller" of the Personal Data you provide us and will process any such Personal Data in accordance with applicable law and the statements contained in this Employment Candidate Privacy Notice.
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Experience in Extract-Transform-Load (ETL) developmentKnowledge of ETL concepts, tools, and data structuresExperience with cloud platforms like Amazon Web Services (AWS), Microsoft Azure, etc.
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Preferred Skills: Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Your expertise in Python, PySpark, ETL processes, CI/CD (Jenkins or GitHub), and experience with both streaming and batch workflows will be essential in ensuring the efficient flow and processing of data to support our clients.
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Experience in crafting data lakehouse solutions in the cloud (Preferably AWS. Alternatively, Azure, GCP). AWS cloud services such as EC2, EMR, RDS, Redshift, Glue, SageMaker (or Azure and GCP equivalents.
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Certification in cloud platforms (e.g., Azure DevOps Engineer Expert, AWS Certified DevOps Engineer). Experience with cloud platforms (e.g., Azure, AWS, GCP.
$143,706 a yearFull-timeRemoteExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Advanced proficiency in the design and implementation of modern data architectures and concepts such as cloud services (e.g., AWS, Azure, GCP), real-time data distribution (e.g., Kafka, Kinesis, DataFlow, Airflow), NoSQL (e.g., MongoDB, DynamoDB, HBase, CosmosDB) and modern data warehouse tools including Snowflake and DataBricks.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure ML (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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Demonstrated experience with other non-AWS cloud services such as, Google Cloud Platform, Microsoft Azure. Demonstrated experience deploying AWS applications with AWS‚ Cloud Development Kit (CDK.
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Experience with provisioning virtual resources and building infrastructures in AWS, Google Cloud, Microsoft Azure, and Digital Ocean commercial clouds. Working knowledge of AWS Cloud Formation, JSON, and Python scripting languages.
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Move data between different cloud storage environments for critical requests. Certification(s) o AWS DevOps Engineer, Solutions Architect, or SysOps Administrator. Demonstrated experience creating and managing AWS resources, including provisioning EC2 instances, writing and deploying Lambda functions, creating and writing to S3, and managing authorization appropriately across resources with IAM policies.
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Demonstrated experience deploying AWS applications with AWS’s Cloud Development Kit (CDK). AWS DevOps Engineer, Solutions Architect, or SysOps Administrator. Work will include developing Amazon Web Services (AWS)-based resources and the Customer needs skills spanning many compute, storage, and networking services.
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Experience with a public cloud (AWS, Microsoft Azure, Google Cloud) Building required infrastructure for optimal extraction, transformation and loading of data from various data sources using AWS and SQL technologies.
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Experience with distributed data platforms and technologies (e.g., Hadoop, Spark, Kafka) and cloud-based data services (e.g., AWS, Azure, GCP) Certifications related to data engineering or cloud platforms.
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