> Markdown version of [/jobs/ext/3878725-real-time-data-streaming-architect](https://www.wearedevelopers.com/jobs/ext/3878725-real-time-data-streaming-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Real Time Data Streaming Architect - **Company:** Darwin Recruitment - **Location:** Amsterdam, Netherlands - **Experience:** Experienced - **Salary:** €5,000.0 - €7,500.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Software Engineering, Data Streaming, Google Cloud, Event Driven Architecture, Kubernetes, Apache Flink, Apache Kafka, OSS/BSS, Api Design, Stream Processing, Microservices - **Published:** October 23, 2026 - **Apply:** https://www.adzuna.nl/details/5937769942 ## About the Role * 10+ years in data/platform/software engineering, with 3-5 years in a leadership role * Deep Apache Kafka expertise - architecture, operations, schema governance, ecosystem (Connect, Streams, Schema Registry) * Hands-on Apache Flink or comparable stream processing * Telco or large-scale enterprise experience - OSS/BSS, network data, or enterprise platforms * Proven track record building or scaling a central streaming platform * Strong architectural thinking - event-driven architecture, microservices, API design, integration patterns * Cloud & containers - AWS/Azure/GCP, Kubernetes, CI/CD * Excellent stakeholder management - C-level to engineering teams * Strong communication - translates technical complexity into business value Experience: * Leadership: Defined and executed a streaming strategy at enterprise scale; built and grew teams; influenced at C-level; drove organisational change from batch-first to streaming-first. * Technical depth: Kafka at scale, cluster architecture, topic design, schema governance, exactly-once semantics, operational reliability; stream processing trade-offs; event-driven patterns; schema evolution without breaking downstream; observability for streaming. * Delivery & operations: Ran streaming platforms in production at telco scale (millions of events/day); improved reliability and reduced incidents; managed vendor and integrator relationships; delivered projects on time and within scope. Business and ecosystem: Connected streaming to business outcomes (network ops, customer experience, security, AI); familiar with telecom data landscape; engaged with industry standards (TM Forum, IDSA, iSHARE); brings a relevant industry network.