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) for high-volume behavioral and monetization data.
• Drive platform evolution, including migrating and decommissioning legacy systems with zero disruption.
• Ensure software engineering excellence through CI/CD for data, automated testing and observability.
• 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, keeping compute costs effective and ROI clear.
• Plan long-term strategy aligned with company OKRs, including AI readiness and real-time event streaming, and manage key vendor relationships.

Required profile

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

Required skills

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

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