Overview
Remote
Depends on Experience
Full Time
Skills
Cloud Computing
Collaboration
Data Management
Google Cloud Platform
Systems Design
Python
Apache Kafka
Authentication
Apache Avro
Firewall
API
Job Details
Job Description
Kafka Messaging Troubleshooter and Kafka Developer
A strong understanding of Kafka architecture, including brokers, topics, partitions, and consumer groups, alongside skills in reading and interpreting logs, monitoring metrics, familiarity with distributed systems concepts, proficiency in programming languages like Java or Scala, and knowledge of network connectivity and configuration to identify and resolve potential problems within a Kafka cluster.
Key skills required for Kafka messaging troubleshooting:
Deep understanding of Kafka architecture: Thorough knowledge of how Kafka components like brokers, topics, partitions, consumer groups, and replication factors work together.
Log analysis: Ability to interpret Kafka logs from producers, consumers, and brokers to identify error messages, warnings, and potential issues.
Monitoring and metrics: Familiarity with monitoring tools to track key Kafka metrics like consumer lag, message throughput, broker CPU usage, and network latency.
Distributed systems knowledge: Understanding of concepts like fault tolerance, data replication, leader election, and distributed consensus to troubleshoot issues related to cluster failures.
Programming language proficiency: Strong coding skills in Java or Scala, as many Kafka applications are written in these languages, allowing you to debug custom producers and consumers.
Network troubleshooting: Ability to diagnose network connectivity issues between brokers and clients, including checking network configurations and firewall rules.
Kafka configuration management: Knowledge of Kafka configuration parameters, including topic creation, partition settings, replication factors, and consumer group settings.
Security understanding: Awareness of Kafka security mechanisms like authentication, authorization, and encryption to troubleshoot related issues.
Troubleshooting tools and techniques: Familiarity with Kafka management tools, command-line utilities, and debugging techniques to investigate and resolve issues.
Consumer lag: Identifying the cause of high consumer lag (e.g., slow processing, insufficient consumers) and adjusting consumer configurations or application logic.
Broker failures: Analyzing logs and metrics to determine the root cause of a broker failure and taking actions like rebalancing partitions or restarting the broker.
Message delivery issues: Investigating missing messages, message duplication, or out-of-order delivery by examining producer and consumer configurations.
Performance bottlenecks: Identifying performance issues related to high message throughput, network congestion, or slow disk I/O and optimizing Kafka settings.
As Kafka Developer
A strong proficiency in Confluent Kafka architecture, a programming language like Java or Scala, expertise in system design, data management skills, and the ability to understand and implement data streaming pipelines. < /div>
Key skills required for Kafka Developer:
Deep understanding of Confluent Kafka: Thorough knowledge of Kafka concepts like producers, consumers, topics, partitions, brokers, and replication mechanisms.
Programming language proficiency: Primarily Java or Scala, with potential for Python depending on the project.
System design and architecture: Ability to design robust and scalable Kafka-based data pipelines, considering factors like data throughput, fault tolerance, and latency.
Data management skills: Understanding of data serialization formats like JSON, Avro, and Protobuf, and how to manage data schema evolution.
Kafka Streams API (optional): Knowledge of Kafka Streams for real-time data processing within the Kafka ecosystem.
Monitoring and troubleshooting: Familiarity with tools to monitor Kafka cluster health, identify performance bottlenecks, and troubleshoot issues.
Cloud integration : Experience deploying and managing Kafka on cloud platforms like AWS, Azure, or Google Cloud Platform.
Distributed systems concepts: Understanding of concepts like distributed consensus, leader election, and fault tolerance.
Security best practices: Knowledge of Kafka security features to implement authentication and authorization mechanisms.
Communication and collaboration: Ability to work effectively with other developers, data engineers, and stakeholders to design and implement Kafka solutions.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.