Senior Flink Engineer

Flink engineering: Designed and delivered production Flink applications using DataStream API. Managed stateful streaming jobs with checkpoints, savepoints, and RocksDB state backends. Handled event-time processing, watermarks, and late/out-of-order events in real-world pipelines.

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Kafka ecosystem: Integrated Flink with Kafka as source and sink. Worked with Schema Registry and evolving schemas. Understands exactly-once guarantees across the Kafka-Flink boundary.

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Operational ownership: Deployed and ran Flink on Kubernetes. Configured HA, monitoring, and alerting. Debugged production issues backpressure, checkpoint failures, state corruption, resource contention.

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Delivery: Worked within CI/CD pipelines for Flink job deployment. Collaborated with platform, data governance, and network teams. Delivered streaming use cases from design through production.

You Will Join

A team of about 8 engineers an international, distributed team working hybrid across Netherlands. The team operates with a high degree of autonomy: you’ll be expected to work independently, take ownership of your workstreams, and contribute without constant direction. The team is helping to grow the company’s real-time streaming capability and to help clients gain better knowledge and insight from their data through streaming.

5+ years as a software/data engineer, with at least 2-3 years hands-on Apache Flink in production

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5+ years in the telecommunications industry - understanding of network data, OSS/BSS, or telco operational systems

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Deep Flink expertise - DataStream API, state management (checkpoints, savepoints, RocksDB), watermarks, event-time processing, exactly-once semantics

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Kafka integration experience - Flink connectors, schema handling (Avro/Protobuf with Schema Registry), out-of-order event handling

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Production Flink deployments - cluster management, JobManager/TaskManager tuning, memory and resource optimisation

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Cloud-native experience - Kubernetes (AKS/EKS/GKE), containerised Flink deployments, CI/CD (ArgoCD, GitHub Actions)

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Strong Java (and ideally Scala or Python) for building and maintaining Flink applications

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Observability - monitoring Flink jobs with Prometheus, Grafana, or similar; understanding of checkpointing metrics and backpressure

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Problem-solving mindset - able to debug complex distributed streaming issues in production

Desirable: Kafka Streams, Flink SQL, Iceberg/lakehouse integration, TM Forum Open APIs, NIS2/GDPR compliance exposure, telecom network telemetry pipelines.

Candidate Experience

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Telecom domain: Built or operated real-time pipelines processing network logs, 5G/4G telemetry, IoT sensor data, or operational data at scale. Understands the unique challenges of telco data volume, velocity, schema evolution, and regulatory context.

A leading telecommunications operator is building its real-time data capabilities on a central streaming platform powered by Apache Kafka and Apache Flink. The platform supports network telemetry, security monitoring, customer analytics, and IoT use cases at telco scale. The organisation is shifting from batch-first to streaming-first, and Flink is core to that transition.