Kafka Architect
1 week ago
Years of experience - 8 to 15 Years
Qualification - B.Tech / MCA
Key Responsibilities :
Design, deploy, and manage Kafka clusters in a production environment, ensuring high availability, scalability, and reliability.
Collaborate with engineering teams to understand data processing requirements and design Kafka solutions that meet their needs.
Optimize Kafka configurations and performance to ensure efficient utilization of resources and low latency data processing.
Develop and maintain Kafka-related tools, utilities, and automation scripts to streamline deployment, monitoring, and management tasks.
Provide guidance and best practices to engineering teams on Kafka usage, data modelling, and integration patterns.
Work closely with operations teams to monitor Kafka clusters, troubleshoot issues, and perform capacity planning.
Stay updated with the latest Kafka features, enhancements, and best practices, and evaluate their applicability to our environment.
Technical skill you should have:
In-depth understanding of Kafka architecture , including brokers, topics, partitions, producers, consumers, and replication.
Knowledge of Kafka distributed nature, fault tolerance, scalability and high availability
Experience with streaming data processing frameworks like Apache Flink, Apache Spark Streaming, or Kafka Streams.
Knowledge of Kafka integration pattern and best practises for integration with various data source
Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
Experience with cloud platforms like AWS, Azure, or Google Cloud Platform .
Relevant certifications such as Confluent Certified Developer or Confluent Certified Administrator.
Knowledge of containerization and orchestration technologies such as Docker and Kubernetes.
Very good knowledge in database technology for both NoSQL DB and SQL DB. Working experience with in-memory DB such as Couchbase would be an advantage.
Establish scalable implementation of data management framework, platform and tools stack.
Good knowledge to develop, implement and managing BAU operational process for data quality functionality and capability such as Data Quality Statistics, Data Quality Scorecards, Data Quality Analysis and Workflows Design
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