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Role

• Build, maintain, and optimize real-time data pipelines to process bid requests, impressions, clicks, and user engagement data.
• Develop scalable solutions using tools like Apache Flink, Spark Structured Streaming, or similar stream processing frameworks.
• Collaborate with backend engineers to integrate OpenRTB signals into our data pipelines and ensure smooth data flow across systems.
• Ensure data pipelines handle high-throughput, low-latency, and fault-tolerant processing in real-time.
• Write clean, well-documented code in Java, Scala, or Python for distributed systems.
• Work with cloud-native messaging and event platforms such as GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka to ensure reliable message delivery.
• Assist in the management and evolution of event schemas (Protobuf, Avro), including data consistency and versioning.
• Implement monitoring, logging, and alerting for streaming workloads to ensure data integrity and system health.
• Continuously improve data infrastructure for better performance, cost-efficiency, and scalability.

Profile (Must have)

• 3-5+ years of experience in data engineering, with a strong focus on real-time streaming systems.
• Familiarity with stream processing tools like Apache Flink, Spark Structured Streaming, Beam, or similar frameworks.
• Solid programming experience in Java, Scala, or Python, especially in distributed or event-driven systems.
• Experience working with event streaming and messaging platforms like GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka.
• Hands-on knowledge of event schema management, including tools like Avro or Protobuf.
• Understanding of real-time data pipelines, with experience handling large volumes of event-driven data.
• Comfortable working in Kubernetes for deploying and managing data processing workloads in cloud environments (AWS, GCP, etc.).
• Exposure to CI/CD workflows and infrastructure-as-code tools such as Terraform, Docker, and Helm.

Nice to have

• Familiarity with real-time analytics platforms (e.g., ClickHouse, Pinot, Druid) for querying large volumes of event data.
• Exposure to service mesh, auto-scaling, or cost optimization strategies in containerized environments.
• Contributions to open-source projects related to data engineering or stream processing.

Benefits

• Competitive salary upon experience
• Comprehensive relocation package (including visa support)
• Swile Lunch voucher
• Gymlib (100% borne by Voodoo)
• Premium healthcare coverage SideCare, for your family is 100% borne by Voodoo
• Child day care facilities (Les Petits Chaperons rouges)
• Wellness activities in our Paris office
• Unlimited vacation policy
• Remote days