Offer summary

Qualifications:

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, or similar., Strong programming experience in Java, Scala, or Python, focusing on distributed systems., Deep understanding of event streaming and messaging platforms such as Kafka, including performance tuning and schema management..

Key responsabilities:

Design, implement, and optimize real-time data pipelines handling billions of events per day.

Build scalable and reliable event ingestion and processing systems using technologies like Kafka and Flink.

Collaborate with backend teams to integrate OpenRTB signals into the data platform in near real-time.

Mentor other engineers and advocate for best practices in streaming architecture and performance.

Job description

Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz.

Team

The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile industry.

Within the Data team, you’ll join the Ad-Network Team which is an autonomous squad of around 30 people. The team is composed of top-tier software engineers, infrastructure engineers, data engineers, mobile engineers, and data scientists (among which 3 Kaggle Masters). The goal of this team is to provide a way for Voodoo to monetize our inventory directly with advertising partners, and relies on advanced technological solutions to optimize advertising in a real-time bidding environment. It is a strategic topic with significant impact on the business.

This role can be done fully remote in any EMEA country.

Role

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.

Profile (Must have)

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.

Nice to have:

Knowledge of stream-native analytics platforms (e.g., Druid, ClickHouse, Pinot).

Understanding of frequency capping, fraud detection, and pacing algorithms.

Exposure to service mesh, auto-scaling, and cost optimization in containerized environments.

Contributions to open-source streaming or infra projects.

Nice to Have

Knowledge of stream-native analytics platforms (e.g., Druid, ClickHouse, Pinot).

Understanding of frequency capping, fraud detection, and pacing algorithms.