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At Capital One, you’ll be part of a big group of builders, breakers, doers and disruptors, who love to solve real problems and meet real customer needs. Lead innovative identity lifecycle management initiatives and a team of developers with deep experience in Identity and Access Management (IAM) to create solutions that help meet regulatory needs for the company and improve the user-experience for our workforce.
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Must have strong analytical and technical skills in computer network defense operations, ability to lead efforts in Incident Handling (Detection, Analysis, Triage), Hunting (anomalous pattern detection and content management) and Malware Analysis.
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As a Quantitative Modeler at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in cloud computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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Experience working with military aviation-based sensors and data (e.g. radar, electronic warfare, electro-optical, infrared, sonar, stores management, air data, fuel data, weapons data, engine data.
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Assessing the impact of recent CrowdStrike release notes and technical alerts, and implementing changes in accordance with client change management policies. Crowdstrike certifications are a big plus.
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This role will provide expertise to support the development of a Big Data / Data Lake system architecture that supports enterprise data operations for the District of Columbia government, including the Internet of Things (IoT) / Smart City projects, enterprise data warehouse, the open data portal, and data science applications.
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Assist in the maintenance of services and procedures for a big data pipeline which includes Kafka, Elasticsearch, and several in house written python/java micro services running on Kubernetes. Experience with any of the following technologies:Kubernetes monitoring, e.g., Prometheus/ GraphanaGPU-based KubernetesSALT for deployment automationElasticsearch, Kibana, and Logstash, specifically admin experienceHelm and HelmfileCloudera usage, including Kafka topic creation/management, Zookeeper, Solr, and FlumeNexus.
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Required 3 Years · Experience with deployment and management of data science tools and modules such as JupyterHub. Required 3 Years · Experience with ETL, data processing, analytics using languages such as Python, Java or R. Required 3 Years · Databricks Certified Data Engineer Professional · Experience with Cloudera Data Platform.
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Experience in CI/CD pipeline and related open-source tools like GIT/Jenkin/CircleCI/SonarQube knowledge in Terraform/Ansible will be big plus. Knowledge of developer tooling across the software development life cycle (task management, source code, building, deployment, test automation and related tools, operations, real-time communication.
$261,500 a yearFull-timeRemoteExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Experience with DevSecOps, CI/CD, IaC and CaC, IT Infrastructure Library, and IT Service Management. Experience with big data analytics, machine learning, artificial intelligence, or anomaly detection.
$154,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Our capabilities range from C5ISR, AI and Big Data, cyber operations and synthetic training environments to fleet sustainment, environmental remediation and the largest family of unmanned underwater vehicles in every class.
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Coordinates IT project management, engineering, maintenance, QA, and risk management. This architecture includes an Databricks, Microsoft Azure platform tools (including Data Lake, Synapse) and data pipeline/ETL development tools (including Streamsets, Azure Data Factory.
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Experience with CI/CD, IaC and CaC, ITIL, and IT Service Management. Preferred Qualifications for the Cyber Engineer:·Experience with cloud security, including AWS, Azure, or GCP.·Knowledge of big data analytics, machine learning, and anomaly detection.
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Proficiency in data pipeline automation and workflow management tools like Apache Airflow or AWS Step Functions. Experience with data lakes and big data technologies (e.g., Apache Spark, Citus.
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Experience with big data technologies (e.g., Hadoop, Spark, Kafka). Strong experience with SQL and database management systems (e.g., MySQL, PostgreSQL, Oracle). Strong understanding of data architecture and data management best practices.
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