Senior Data Engineer, Voodoo

• Extensive experience in data engineering or platform engineering roles
• Strong programming skills in Python and Java
• Strong experience with modern data stacks (e.g., Spark, Kafka, DBT, Airflow, Lakehouse)
• Deep understanding of distributed systems, data architecture, and performance tuning
• Experience with cloud platforms (AWS, GCP, or Azure) and Infrastructure-as-Code tools (Terraform, CloudFormation, etc.)
• Solid experience operating data services in Kubernetes, including Helm, resource tuning, and service discovery
• Strong understanding of data modeling, data governance, and security best practices
• Knowledge of CI/CD principles and DevOps practices in a data environment
• Excellent problem-solving, communication, and leadership skills

Desirable

• 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
• Design, develop, and maintain scalable, secure, and high-performance data platforms
• Build and manage data pipelines (ETL/ELT) using tools such as Apache Airflow, DBT, SQLMesh or similar
• Architect and optimize lakehouse solutions (e.g., Iceberg)
• Lead the design and implementation of data infrastructure components (streaming, batch processing, orchestration, lineage, observability)
• Ensure data quality, governance, and compliance (GDPR, HIPAA, etc.) across all data processes
• Automate infrastructure provisioning and CI/CD pipelines for data platform components using tools like Terraform, CircleCI, or similar
• Collaborate cross-functionally with data scientists, analytics teams, and product engineers to understand data needs and deliver scalable solutions
• Mentor experienced data engineers and set best practices for code quality, testing, and platform reliability
• Monitor and troubleshoot performance issues in real-time data flows and long-running batch jobs
• Stay ahead of trends in data engineering, proactively recommending new technologies and approaches to keep our stack modern and efficient