Senior Data Engineer - Feature Platform

About Voodoo

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 our gaming and consumer apps, allowing Voodoo to stay at the forefront of the mobile gaming industry.

The Voodoo Ad‑Network

The Voodoo Ad‑Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top‑tier ad network services. Our primary goal is to leverage Voodoo’s massive first‑party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social‑network monetization following our recent acquisition of BeReal.

The Feature Platform Team

The Feature Platform Team is the foundational infrastructure engine powering our ML Ads Recommendation capabilities. We build and maintain the unified data layer for machine‑learning features, accelerating the ML lifecycle by providing a scalable, highly available architecture for computing, storing, and serving batch, real‑time, and on‑demand features. We also explore new data signals and feature‑engineering opportunities to push targeting performance.

Role

We’re looking for a Senior Data Engineer to join the Feature Platform Team. This hybrid position can be based in Helsinki, Paris, or Strasbourg.

In this role you will manage both offline and online components of our machine‑learning architecture, bridging massive‑scale data processing with high‑load online services that handle sub‑second feature updates.

• Architectural ownership: take end‑to‑end ownership of projects from ideation to production release, including scoping, timing, design, and benchmarking.
• Proactive data innovation: partner across the data lifecycle to discover high‑impact feature opportunities and unlock new data signals with upstream teams.
• ML infrastructure & feature platform: collaborate to design, scale, and optimize core components for both offline training and online inference, including the Feature Store and on‑demand training‑dataset engines.
• Pipeline engineering: build and maintain mission‑critical pipelines for batch processing and real‑time streams, ensuring sub‑second updates.
• High‑performance online services: build and maintain low‑latency, high‑availability backend applications for model serving.
• Scalability & performance: work with infrastructure teams to guarantee reliability, security, and scalability for an ad‑network ecosystem.
• Agile collaboration: thrive in a fast‑paced, agile environment working with back‑end developers, data scientists, and product managers.
• Mentorship & team culture: share knowledge and support teammates to grow professionally.

Profile

• 6+ years of proven experience in data engineering, machine‑learning engineering, or back‑end engineering at a high‑scale environment.
• Big Data & streaming mastery: extensive hands‑on experience with Flink or Spark at scale, and deep expertise with Flink (or similar stateful streaming platform) for our real‑time architecture.
• Coding proficiency: advanced expertise in Python for robust ETL pipelines and custom feature‑definition SDKs/DSLs.
• Experience or willingness to work with Golang for high‑performance, low‑latency back‑ends; familiarity with Java is highly valued for Flink workloads.
• Data architecture: deep understanding of modern Data Lakehouse design, open‑table formats such as Iceberg, optimisation techniques, and data modelling.
• Cloud & DevOps: strong hands‑on experience with a major cloud platform (AWS preferred), and knowledge of DBT for data‑pipeline transformation.
• ML production awareness: solid grasp of challenges in running ML models in production, including Feature Store interactions, training‑serving skew mitigation, and model monitoring.
• System design: familiar with scalability, high‑availability, low‑latency API design, and security best practices.

Our Stack

• Languages: Python (ETL & SDKs), Golang (high‑performance services), Java (Flink)
• Processing & orchestration: Spark, Flink (real‑time), Airflow, DBT
• Storage & infrastructure: Apache Iceberg, Amazon Web Services (AWS), Kubernetes, Terraform

Benefits

• Competitive salary based on experience
• Swile lunch voucher
• Gymlib (100% covered by Voodoo)
• Premium healthcare coverage with SideCare, 100% covered for you and your family
• Wellness activities in our Paris office
• Remote Fridays