In short

We are seeking a highly skilled and motivated Streaming Platform Engineer to join the Data Streaming Platform team. This is a unique hybrid role that combines the disciplines of platform, software, and data engineering to build, scale, and maintain our high-performance, real-time data streaming platform. The ideal candidate should have a passion for architecting robust, scalable systems to enable data-driven products and services at massive scale.

Your mission

• Design, build, and maintain the core infrastructure for our real-time data streaming platform, ensuring high availability, reliability, and low latency.
  
  
• Implement and optimize data pipelines and stream processing applications using technologies like Apache Kafka, Apache Flink, and Spark Streaming.
  
  
• Collaborate with software and data engineering teams to define event schemas, ensure data quality, and support the integration of new services into the streaming ecosystem.
  
  
• Develop and maintain automation and tooling for platform provisioning, configuration management and CI/CD pipelines.
  
  
• Champion the development of self-service tools and workflows that empower engineers to manage their own streaming data needs, reducing friction and accelerating development.
  
  
• Monitor platform performance, troubleshoot issues, and implement observability solutions (metrics, logging, tracing) to ensure the platform's health and stability.
  
  
• Stay up-to-date with the latest advancements in streaming and distributed systems technologies and propose innovative solutions to technical challenges.
  
  
  

Your story

This is a hybrid role, and we understand that candidates may not have experience with every single technology listed. We encourage you to apply if you have a strong foundation in a majority of these areas.

• Streaming Platforms & Architecture: Strong production experience with Apache Kafka and its ecosystem (e.g., Confluent Cloud, Kafka Streams, Kafka Connect). Solid understanding of distributed systems and event-driven architectures and how they drive modern microservices and data pipelines.
  
  
• Real-Time Data Pipelines: Experience building and optimizing real-time data pipelines for ML, analytics and reporting, leveraging technologies such as Apache Flink, Spark Structured Streaming, and integration with low-latency OLAP systems like Apache Pinot.
  
  
• Platform Infrastructure & Observability: Hands-on experience with major Cloud Platforms (AWS, GCP, or Azure), Kubernetes and Docker, coupled with proficiency in Infrastructure as Code (Terraform). Experience integrating and managing CI/CD pipelines (GitHub Actions) and implementing comprehensive Observability solutions (New Relic, Prometheus, Grafana) for production environments.
  
  
• Programming Languages: Proficiency in at least one of the following: Python, Typescript, Java, Scala or Go.
  
  
• Data Technologies: Familiarity with data platform concepts, including data lakes and data warehouses.