Job Description

Rakuten International oversees 7 businesses with over 4,000 employees globally. The brand is recognized for its leadership and innovation in e-commerce, digital content, advertising, entertainment and communications, bringing the joy of discovery and access to more than 1 billion members across the world. Our teams deliver on the company’s mission to delight merchants and customers through innovation, optimism, and teamwork.

Rakuten Viki is a global entertainment streaming platform that specializes in Asian content. Our platform enables millions of viewers to discover and enjoy primetime shows and movies, subtitled in over 150 languages. Headquartered in San Mateo, California, we also have offices in Singapore, Seoul, and Shanghai, ensuring a strong global presence and a deep connection to the heart of Asian entertainment. Our platform is home to a large and loyal community of fans who share a passion for Asian culture and entertainment. Join us in our mission to bridge cultures and connect the world to Asian entertainment. At Rakuten Viki, we offer a chance to be part of a global community that celebrates culture, creativity, and connection.

About the Data Engineering Group:
The Data Platform team manages Viki's core data infrastructure. Right now, we are on a newly built unified Data Lakehouse, so teams can build data products on top of it. Our job is to turn raw user behavior and monetization data into clean tables using tools of the trade like dbt, Airflow. We are in the middle of a major build phase to make setup faster, cost-effective, and ready for ML models and advanced self serve analytics use cases. The team manages the infrastructure, the code, the production setup, the automation within our workflows and everything else related to it.

Reporting to the Director, Platform Engineering, we are in search of a Manager, Data Engineering to lead the development of our next-generation data ecosystem. The ideal candidate is someone who has been a hands-on builder, who can lead a team of talented engineers through our platform evolution, enforce the engineering standards, and bridge the gap between complex data architecture and business-critical product needs. They also have a keen eye on continuously improving the way the team works and raising the bar for everyone around.

Key Responsibilities:

• Lead, mentor, and grow a high-performance team of data engineers, fostering a culture of operational excellence and technical rigor.

• Architect and scale our Data Lakehouse (AWS S3, Iceberg, EMR) and transformation frameworks (dbt) to support high-volume behavioral and monetization and other business data.

• Drive the platform evolution , overseeing the successful migration and decommissioning of legacy systems while ensuring zero disruption to critical business workflows.

• Drive engineering excellence by using the robust CI/CD for data, automated testing, and observability frameworks to ensure platform reliability.

• Institutionalize Data Governance by operationalizing metadata management (DataHub), data quality rules, and automated compliance/privacy workflows (Eg. GDPR/CCPA).

• Enable Self-Serve Analytics by rationalizing our BI layer (Looker) and implementing data activation patterns (e.g., Reverse ETL via Census).

• Manage operational health & FinOps , ensuring cost-effective compute and clear ROI attribution across business domains.

• Plan and execute long-term strategies in line with company OKRs, including AI readiness and real-time event streaming capabilities.

• Manage key vendor relationships to ensure contracts compliance and maximize business value.

Requirements:

• B.S. or M.S. in Computer Science or a related field.

• 3+ years of experience managing or leading cross-functional data engineering teams.

• 10+ years of overall experience building scalable, high-quality data solutions and distributed systems.

• Deep expertise in Cloud Data Architecture: Proven experience with Cloud based setup and common data platform tools such as Airbyte, Map Reduce, Airflow, Kafka.

• Strong Engineering Foundation: Hands-on experience in data engineering development cycle, data modeling, infrastructure-as-code, and managing complex streaming/batch pipelines (e.g., Snowplow, Airbyte).

• Governance & Quality: Experience implementing data quality frameworks, lineage/metadata tools (e.g., DataHub), and regulatory compliance (GDPR) at scale.

• Strategic Stakeholder Management: Ability to translate complex technical architectural choices into business value for product and marketing stakeholders.

Preferred Qualifications:

• Experience managing a data platform end to end.

• Knowledge and expertise in AWS-native stacks, specifically Apache Iceberg , dbt , Airflow , and EMR/EKS

• Experience with Data Modeling or domain-oriented data ownership models.

• Experience with Reverse ETL (e.g., Census) and experimentation platforms (e.g., Statsig).

• Deep understanding of FinOps and cloud cost-optimization strategies for large-scale data environments.

• Previous experience in automating simple workflows and a willingness to get into agentic AI to automate away repeatable tasks.

Rakuten provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type. Rakuten considers applicants for employment without regard to race, color, religion, age, sex, national origin, disability status, genetic information, protected veteran status, sexual orientation, gender, gender identity or expression, or any other characteristic protected by federal, state, provincial or local laws.