Senior Data Engineer - Streaming Platform

Posted 7 days agoViewed

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💎 Seniority level: Senior, 2+ years

📍 Location: EMEA countries

🔍 Industry: Mobile Games and Apps

🏢 Company: Voodoo

⏳ Experience: 2+ years

🪄 Skills: Backend DevelopmentPythonSQLGCPJavaKafkaKubernetesAlgorithmsData engineeringData StructuresSparkCI/CDRESTful APIsLinuxTerraformMicroservicesScalaData modelingDebugging

Requirements:

Extensive experience in data or backend engineering, with at least 2+ years building real-time data pipelines.
Proficiency with stream processing frameworks like Flink, Spark Structured Streaming, Beam, or similar.
Strong programming experience in Java, Scala, or Python, with a focus on distributed systems.
Deep understanding of event streaming and messaging platforms such as GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka — including performance tuning, delivery guarantees, and schema management.
Solid experience operating data services in Kubernetes, including Helm, resource tuning, and service discovery.
Experience with Protobuf/Avro, and best practices around schema evolution in streaming environments.
Familiarity with CI/CD workflows and infrastructure-as-code (e.g., Terraform, ArgoCD, CircleCI).
Strong debugging skills and a bias for building reliable, self-healing systems.

Responsibilities:

Design, implement, and optimize real-time data pipelines handling billions of events per day with strict SLAs.
Architect data flows for bidstream data, auction logs, impression tracking and user behavior data.
Build scalable and reliable event ingestion and processing systems using Kafka, Flink, Spark Structured Streaming, or similar technologies.
Operate data infrastructure on Kubernetes, managing deployments, autoscaling, resource limits, and high availability.
Collaborate with backend to integrate OpenRTB signals into our data platform in near real-time.
Ensure high-throughput, low-latency processing, and system resilience in our streaming infrastructure.
Design and manage event schemas (Avro, Protobuf), schema evolution strategies, and metadata tracking.
Implement observability, alerting, and performance monitoring for critical data services.
Contribute to decisions on data modeling and data retention strategies for real-time use cases.
Mentor other engineers and advocate for best practices in streaming architecture, reliability, and performance.
Continuously evaluate new tools, trends, and techniques to evolve our modern streaming stack.

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