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Kafka Devops Engineer Jobs

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Sr. Software Engineer, Big Data
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United States , Irvine
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103170.00 - 158873.00 USD / Year
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Hyundai AutoEver America
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Platform Engineer - Cloud
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India , Pune
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Barclays
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Senior Java Engineer
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Mexico , Guadalajara
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Senior Solutions Engineer – Big Data & Data Infrastructure
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Israel , Tel Aviv
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VAST Data
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Senior Java Engineer
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Mexico , Guadalajara
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Zoolatech
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Data Engineer
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United Kingdom
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360 Resourcing Solutions
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Data Engineer
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United States , McLean
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Middle Java Engineer
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Middle Java Engineer
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Poland , Wroclaw
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Junior Java Engineer
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Senior Data Engineer
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Netherlands
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Levy Professionals
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Senior Data Engineer
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Netherlands , Amstelveen
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Big Data Scala Engineer
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India , Pune
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Citi
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Java Springboot Senior Technical Lead
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India , Noida; Chennai
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Apps Development Technology(Databases) Lead Analyst
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Manager, Software Engineering (User Systems)
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United States , Boston
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142800.00 - 209500.00 USD / Year
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Data Architect
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United States
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InData Labs
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Python Developer
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Java Full Stack Developer
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Junior Software Engineer
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India , Bangalore
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A Kafka DevOps Engineer is a specialized IT professional who sits at the critical intersection of data streaming and modern software operations. This role is dedicated to building, scaling, and maintaining robust, high-performance Apache Kafka ecosystems that serve as the central nervous system for real-time data in an organization. For professionals seeking Kafka DevOps Engineer jobs, this career path offers a unique blend of deep technical expertise in a specific platform and the broad, automation-focused mindset of DevOps and Site Reliability Engineering (SRE). These engineers are the guardians of data flow, ensuring that mission-critical event streams are reliable, secure, and highly available. The typical responsibilities of a Kafka DevOps Engineer are comprehensive, covering the entire lifecycle of the Kafka platform. They are responsible for the design and deployment of Kafka clusters, often in cloud or hybrid environments, ensuring configurations are optimized for low latency and high throughput. A core part of their day-to-day involves writing Infrastructure as Code (IaC) using tools like Terraform and Ansible to automate provisioning and management, treating the Kafka infrastructure as a programmable asset. They implement and maintain sophisticated monitoring and alerting systems using tools like Prometheus, Grafana, or commercial APM solutions to gain deep visibility into cluster health, topic performance, and consumer lag, enabling proactive issue resolution. Furthermore, these engineers are tasked with performance tuning, which includes optimizing partition strategies, replication factors, and broker configurations to handle massive data volumes. Security is paramount; they implement and manage robust security protocols including SSL/TLS encryption for data in transit, authentication mechanisms like mTLS or Kerberos, and fine-grained access control through Role-Based Access Control (RBAC). They also integrate Kafka seamlessly into CI/CD pipelines, automating the deployment of Kafka connectors, schemas, and client applications. Other common duties include capacity planning for future growth, conducting disaster recovery drills, and providing expert guidance to development teams on Kafka best practices for building efficient producers and consumers. The typical skill set required for Kafka DevOps Engineer jobs is both deep and wide. A strong foundation in Apache Kafka itself is non-negotiable, including deep knowledge of its architecture, the Kafka ecosystem (like Kafka Connect, KSQL, and Schema Registry), and ZooKeeper. Proficiency in programming and scripting languages such as Python, Java, or Go is essential for automation and tooling. Hands-on experience with containerization and orchestration platforms, particularly Kubernetes, is now a standard requirement for deploying and managing Kafka in modern cloud-native environments. A firm grasp of DevOps fundamentals is critical, including expertise in CI/CD tools (Jenkins, GitLab CI), Infrastructure as Code (Terraform, Ansible), and monitoring stacks. Solid understanding of networking, Linux operating systems, and cloud platforms (AWS, Azure, GCP) rounds out the technical profile. Beyond technical acumen, successful professionals in these jobs possess strong problem-solving abilities, excellent communication skills to collaborate with both development and operations teams, and a proactive mindset focused on building resilient, self-healing systems. This role is ideal for those who thrive on ensuring that real-time data pipelines are not just functional, but performant, secure, and scalable, making them highly sought-after in today's data-driven economy.

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