At the core of our architecture in PayPay, we use Kafka for high-performance data streaming, and every payment that goes through the app is handled by multiple topics in Kafka. To keep up with the challenges of our growing product and future expansion, we are expanding our Streaming Platform team and are looking for an engineer with deep experience in Kafka to help lead the next stage.
The role involves strengthening our existing platform to bring a truly event-driven architecture to our development process, while adding performance and resilience to the existing application. A key part of our current roadmap includes migrating our self-hosted Kafka clusters to KRaft mode and operating Kafka both in self-managed form and via AWS MSK. We are looking for someone who is not only hands-on with Kafka internals, but also confident operating it at scale in production environments.
This is a senior role but still very hands-on , an excellent opportunity to build and modernize the streaming infrastructure that powers payments for over 70 million users.
Tech Stack
- Java, Python, Go
- Apache Kafka, Elasticsearch/Opensearch, AWS MSK, Athena, Glue
- Docker, Kubernetes, ArgoCD, Argo rollouts, Argo workflows, Artifactory, AWS, GCP
- GitHub, GitHub Actions, Jenkins, Terraform, Ansible, Microservices, GitOps
- Logstash, Fluent-bit, Vector, Victoria Metrics, Grafana, Prometheus
- Slack, Zoom, Confluence, JIRA
Responsibilities
- Provide architecture, development, and operations ownership for Apache Kafka in production (self-hosted and AWS MSK)
- Deploy, manage and operate high-availability Kafka clusters
- Improve resilience, scalability and failure-mode readiness of Kafka infrastructure
- Monitoring and alerting of Kafka for reliability, throughput, and latency
- Perform broker tuning and partition strategy guidance with production scale in mind
- Partner with application teams to guide Kafka topic design, schema management, and best practices
- Contribute to the development of scalable, secure, and observable data pipelines
- Automate platform and operations workflows using IaC and automation tools
Requirements
- Minimum of 2 years of engineering experience with Apache Kafka in production environments
- Minimum of 2 years of experience in AWS, Terraform, Ansible and Linux Administration
- Strong hands-on experience with Kafka cluster operations, including setup, tuning, and maintenance
- Experience with Kafka authentication and authorization operations
- Familiarity with AWS cloud platform, especially Amazon MSK (Managed Streaming for Kafka)
* We heavily rely on AWS, so those without previous AWS experiences must expect to catch up with it after joining us - Proficiency in one or more general-purpose programming languages (e.g., Python, Java, Go)
- Experience with infrastructure automation and configuration management tools, such as Terraform or Ansible
- Understanding of modern system design using microservice architecture
- Working knowledge of Git and CI/CD tools
- Ability to take ownership, solve problems fast and operate proactively
Nice to haves
While not specifically required, tell us if you have any of the following.
- Experience in running and scaling Apache Kafka both self-hosted and in managed cloud environments (e.g., AWS MSK)
- Experience migrating or operating Kafka in KRaft mode (no ZooKeeper)
- Experience in data replicate between Kafka clusters, enabling seamless data migration, disaster recovery, and cross-region data synchronization
- Exposure to Kafka security, including ACLs, TLS, SASL, and IAM-based auth on MSK
- Strong Knowledge of Kafka internals, including broker tuning, partitioning, replication, and fault tolerance
- Contributions to Kafka-related open source projects or community involvement
- Experience with Kafka Connect, Kafka Streams, or other stream processing frameworks
- Experience guiding cross-team adoption of Kafka in microservice architectures
- Experience in operating distributed systems
- Working experience in a full remote environment
- Bachelor’s or Master’s Degree in Computer Science or a related field
