Senior Data Engineering Manager

Rakuten Viber is one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make people’s lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.

As an Engineering Manager in the data department, you'll build and scale our data platform and data apps that powers our business insights. You'll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.

If you’re passionate about data engineering and driving business growth through insights, we’d love to hear from you!

• Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
• Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
• Architect and scale batch and streaming pipelines powering core business and ML use cases.
• Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
• Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

• 3+ years of data engineering management leading architecture and strategy
• 6+ years of experience building large-scale (at least 5 million events a day) distributed systems in Data Engineering, ML Engineering, or Software Engineering roles.
• Proven ownership of production-grade data or ML platforms, including delivery and adoption across R&D and Product stakeholders.
• Hands-on experience building and operating high-scale distributed data systems (Spark, Storm, Flink) in production.
• Strong experience with Java and Python in AWS cloud environments.

• Proven track record leading multi-disciplinary teams and driving measurable business impact through data/ML systems.
• Experience building ML platforms, feature stores, or self-serve data infrastructure at scale.
• Deep experience with modern ML/infra stack (PyTorch, TensorFlow, SageMaker, Kubernetes, Argo).
• Experience with modern data lakehouse and analytics stack (Iceberg, Athena, ClickHouse, data catalogs, data quality frameworks).
• Experience deploying LLM-based systems or AI-driven infrastructure in production environments.