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Experience with provisioning virtual resources and building infrastructures in AWS, Google Cloud, Microsoft Azure, and Digital Ocean commercial clouds. Far Ridgeline Engagements LLC (a QinetiQ US Company) is seeking an experienced cloud data engineer/developer to help lead a growing team with a US Department of Defense operations support project developing modern, cloud-based solutions to empower customers to securely store and analyze data.
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4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) - You will develop applications from ground up using a modern technology stack such as Scala, Spark, Postgres, Angular JS, and NoSQL.
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You will continuously integrate and ship code into our on premise and cloud Production environments. - 4+ years of data warehousing experience (Redshift or Snowflake) - 4+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL.
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Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing. In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies.
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2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) Use cutting edge technologies such as Python, Scala, Spark, and a variety of AWS services to develop modern data pipelines supporting Machine Learning and Artificial Intelligence.
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3+ years of experience with a public cloud (e.g., Microsoft Azure, AWS, Google Cloud). Use cutting edge technologies such as Python, Scala, Spark, and a variety of Cloud services to develop modern data pipelines supporting Machine Learning and Artificial Intelligence.
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Advanced knowledge of cloud computing technologies such as: Apache Spark, Azure Data Factory, Azure DevOps, Azure Client (Machine Learning), Hadoop, Microsoft Azure, Databricks, AWS, Google Cloud.
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5+ years of experience with modern Cloud computing technologies: AWS and Microsoft Azure. Experience with cloud AI ML capabilities like AWS, Databricks.
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Preference for candidate experienced with cloud AI Client capabilities (e.g., AWS, Databricks) Knowledge of modern software design patterns including microservice design and edge computing.
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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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Demonstrates advanced functional knowledge of data visualization libraries such as matplotlib or ggplot2; knowledge of other visualization tools such as Microsoft Power BI and Tableau. Leverage a broad set of modern technologies – including Python, R, and Spark – to analyze and gain insights within large data sets.
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Experience with cloud AI ML capabilities like AWS, Databricks is a plus. Knowledge of modern software design patterns, including microservice design or edge computing. Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
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Familiar with Data Integration, Data Governance, and Data Warehousing. Understanding of data models, large datasets, business/technical requirements, BI tools, statistical programming languages, and libraries.
$90,000 - $145,000 a yearFull-timeExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Working knowledge of AWS Cloud Formation, JSON, and Python scripting languages. Exposure to serverless provisioning such as AWS Lamda or Docker containers with Kubernetes. Bachelor's Degree in Computer Science, Computer Engineering, Information Technology or related STEM program, or four years of equivalent level experience as a Cloud Systems Engineer.
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