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Desire proficiency in cloud-based computing and data platforms (Azure, Google Cloud, BigQuery, Snowflake, DataBricks) Advanced level skill with SQL, Excel, data visualization (Tableau preferred), query tuning, various database models (normalized / dimensional / data vault / etc.
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Experience with cloud computing environments (GCP) and Data/ML platforms (Databricks, Spark). Master’s degree or PhD in Computer Science, Statistics, Applied Mathematics, or a related field, with at least 5 – 7 years’ experience in data science or a similar role.
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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. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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5+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) 4+ years of data warehousing experience (Redshift or Snowflake.
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KEY QUALIFICATIONS: Python, SQL Data processing, ML experience Data Warehouse knowledge ETL pipeline knowledge Data Engineering Data Science Data Analytics, Data Mining PLEASE HAVE KNOWLEDGE OF ONE OR MORE OF THE FOLLOWING TECHNICAL SKILLS: Kubernetes, Tableau, PowerBi MongoDB Microsoft Cloud Services like Azure and AKS Snowflake SQL Server data platform Software development (including design, implementation, testing, and change management.
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Experience with distributed data and computing tools, including Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. As a big Data Engineer at Booz Allen, you'll use your skills and experience to implement data engineering activities on some of the most mission-driven projects in the industry.
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Proficiency with distributed computing frameworks and cloud platforms (AWS, Azure, Google Cloud). As a Data Engineer, you'll design, develop, and optimise data pipelines on a Databricks-based platform, Symphony.
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Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing. Intimately familiar with data technologies, data architecture frameworks, cloud platforms and the emerging trends in technological advancements.
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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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In computer science, we seek candidates who can contribute to Software Engineering, Cloud Computing, Mathematical and Statistical Foundations of Computing, Quantum Computing, High-Performance Computing, Theory and Algorithms, Artificial Intelligence, and Databases.
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Extensive experience in data sciences, research computing, computer science, or related computational or basic sciences research fields. Experience in managing research computational and data science needs to develop, create, and sustain the UCSC research information ecosystem for the next decade.
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Senior Manager, Data Engineering (Python, AWS, Airflow) 4+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) As a Capital One Senior Manager Data Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One.
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Utilize cloud platforms (primarily AWS) to deploy data solutions that leverage native services such as S3, Lambda, Redshift, Glue, Airflow, or equivalent third party SAAS services like Snowflake etc for data lake/data warehousing patterns.
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Science Led, Enterprise Driven – Accelerating Quantum Computing. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With approximately 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
$151,000 a yearFull-timeExpandApply NowActive JobUpdated 21 days ago - UpvoteDownvoteShare Job
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Experience with cloud and modern data science tools and platforms such as Databricks MLFlow, GCP BigQuery, AWS SageMaker, Kubeflow, and Azure ML. Lead and participate in solutioning sessions, working hand-in-hand with businesses/clients, translating business objectives into actionable machine learning/data science use casesWhat You'll Bring.
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