Engineering Manager, Data Platform

About the role

Rakuten Viki, a global entertainment streaming platform specializing in Asian content, is looking for an Engineering Manager, Data Platform to report to the Director, Data. The Data Platform team manages Viki's core data infrastructure on a newly built unified Data Lakehouse, turning raw user behavior and monetization data into clean tables with tools such as dbt and Airflow. The manager leads the team through the platform's evolution, enforces engineering standards and bridges complex data architecture with business needs.

Key responsibilities

• Lead, mentor and grow a team of data engineers skilled in software engineering best practices.
• Architect and scale the Data Lakehouse (AWS S3, Iceberg, EMR) and transformation frameworks (dbt).
• Oversee migration and decommissioning of legacy systems with zero disruption to business workflows.
• Use CI/CD for data, automated testing and observability to ensure platform reliability.
• Institutionalize data governance with metadata management (DataHub), data quality rules and compliance workflows (e.g. GDPR/CCPA).
• Enable self-serve analytics by rationalizing the BI layer (Looker) and implementing data activation patterns (e.g. reverse ETL via Census).
• Manage operational health and FinOps, ensuring cost-effective compute and clear ROI attribution.
• Plan long-term strategy aligned with company OKRs, including AI readiness and real-time event streaming.
• Manage key vendor relationships and ensure contract compliance.

Required profile

• B.S. or M.S. in Computer Science or a related field.
• 3+ years of experience managing or leading cross-functional software engineering teams.
• 10+ years of overall experience building scalable data solutions and distributed systems.
• Ability to translate technical choices into business value for product and marketing stakeholders.

Required skills

• Cloud data architecture: Airbyte, MapReduce, Airflow, Kafka.
• Data engineering: data modeling, infrastructure-as-code, streaming and batch pipelines (e.g. Snowplow, Airbyte).
• Data quality frameworks, lineage and metadata tools (e.g. DataHub), and regulatory compliance (GDPR).

Preferred: end-to-end data platform management, AWS-native stacks (Iceberg, dbt, Airflow, EMR/EKS), reverse ETL (Census), FinOps, and interest in agentic AI.