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Experience with cloud computing platforms and data warehouse solutions such as AWS, Azure, Snowflake, and Microsoft SQL Server. Experience with BigQuery, Snowflake, Microsoft SQL Server.
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Experience with a public cloud, including AWS, Microsoft Azure, or Google Cloud. Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka.
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At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud) 4+ years of experience with a public cloud (AWS, Microsoft Azure, 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 is a plus. 3+ years of experience with artificial intelligence, data science, ML engineering, data research, software engineering, or data analytics.
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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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Experience with provisioning virtual resources and building infrastructures in AWS, Google Cloud, Microsoft Azure, and Digital Ocean commercial clouds. Exposure to serverless provisioning such as AWS Lamda or Docker containers with Kubernetes.
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Experience with cloud computing (AWS, Microsoft Azure, Google Cloud) 3+ years of experience with AWS, GCP, Azure, or another cloud service. Data processing/data transformation using ETL/ELT tools such as DBT (Data Build Tool), or Databricks.
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Experience with a public cloud (AWS, Microsoft Azure, Google Cloud) Experience operating production workloads on Oracle/PostgreSQL (preferred) Solid understanding of data elements and their relationships between the source and target systems Experience working on real-time data and streaming applications.
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Utilize programming languages like Java, Scala and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. 4+ years of data warehousing experience (Redshift or Snowflake.
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Center 1 (19052), United States of America, McLean, VirginiaSenior Manager, Data Engineering (Spark, AWS) 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.
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One or more of the following certifications: AWS Certified Data Engineer - Associate Data Science Council of America (DASCA) Senior Data Architect (SDA) Certification Microsoft Certified: Azure Data Engineer Associate Google Professional Data Engineer Certification SAS Certified Big Data Professional IBM Certified Data Architect – Big Data.
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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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Working knowledge of AWS Cloud Formation, JSON, and Python scripting languages. Much of our work contributes to innovative research in the fields of sensor science, signal processing, data fusion, artificial intelligence (AI), machine learning (ML), and augmented reality (AR.
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Cloud Computing Certifications such as AWS Certified Solutions Architect or Microsoft Certified: Azure Solutions Architect Expert Certification. As a digital transformation architect and leader on our team, you'll use your expertise to transform Azure or AWS cloud operations by implementing an enterprise allocation strategy and FinOps best practices.
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Demonstrated experience with other non-AWS cloud services such as, Google Cloud Platform, Microsoft Azure. Demonstrated experience creating, managing, and working in AWS resources (EC2, Lambda, S3, IAM, Sagemaker, etc.
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