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Should be very good in hands on programming using Java, Spring, Kafka, Python, REST Services, Web API architecture, design and development. on Development on AWS/Azure/GCP, Java, Kafka, REST/JSON, Spring, Data platforms and API platforms.
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Hands-on experience in configuring and deploying containerized services utilizing Docker and Kubernetes, across leading cloud service providers such as AWS, Azure, and GCP. - Get on the Rocketship Early: You can help shape the direction of the engineering org and become a mentor/leader faster here than almost anywhere else.
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Ideally 10+ years of hands-on design and development experience in several of the relevant technology areas, preferably in a cloud environment (Lakehouse architecture, Azure Data Lake, Scala, Databricks, Spark/SparkSQL, Spring Boot, ReactJS, Kubernetes, Postgres.
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Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Be working in the Card Partnerships Conversion tech team supporting Partner Conversions into Capital One. Looking for someone who is passionate about data and has experience implementing scalable data architectures along with prior hands-on data engineering experience leveraging Scala or Python in building ETL pipelines.
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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) As a Capital One Senior Lead Data Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One.
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Minimum 7 years of designing and implementing an operational production grade large-scale data solution on Microsoft Azure Snowflake Data Warehouse. Strong understanding or Snowflake on Azure Architecture, design, implementation and operationalization of large-scale data and analytics solutions on Snowflake Cloud Data Warehouse.
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Responsibilities:Provide technical expertise and guidance on the selection, and hands-on implementation, and optimization of big data platforms, tools, and technologies across multiple cloud environments (e.g., AWS, Azure, GCP, Snowflake, etc.
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Must have hands-on technical experience with core DTCC functions such as clearing and settlement for investment accounts. Certifications such as TOGAF, AWS Certified Solutions Architect, or Microsoft Certified: Azure Solutions Architect are desirable but not mandatory.
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Hands on experience working with Azure Cloud services like Azure Data Factory, Azure Data Lake Storage, Azure Even hub, Azure SQL, Azure Analytical services, Azure Cosmos DB.
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The ideal candidate will have a strong background in SQL Server development, hands-on experience with Azure Pipeline, and proficiency in working with Databricks for data processing and analytics.
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Hands-on experience with big data technologies such as Apache Spark, Hadoop, and/or Databricks. Experience implementing and optimizing data solutions on Azure, including Azure Data Factory, Azure Synapse Analytics, and Azure Databricks.
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Hands-on experience with Azure Enterprise-scale reference architecture IaaS/PaaS/SaaS. Hands-on experience with IaC tools like ARM, Terraform, Ansible, PowerShell, Python, AZCLI, GitHub.
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Hands-on experience in Azure DevOps Administration. Develop and configure DevOps solutions on Azure platform utilizing native Azure services. Knowledge of Kubernetes (AKS) and DevOps tools like TeamCity, TFS, Azure DevOps, Git, SVN, Veracode, and SonarQube.
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Hands on experience managing IT Infrastructure solutions which includes operating systems, networks, DNS, firewalls, SSO, MFA, AWS, Azure, Active Directory, IaaS, PaaS, and SaaS. Coordinate with firm IT Operations teams, including Systems, Network, Dev-Ops and Help Desk teams on implementation and support of key Microsoft/Azure/ O365 and AWS initiatives.
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