Engineering Manager, Data Engineering

Rakuten Kobo Inc.; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

Rakuten Kobo Inc. is seeking a Manager of Data Engineering in Singapore to lead the development of innovative data ecosystems. This role demands expertise in cloud data architecture and a strong background in building high-quality data solutions.

The ideal candidate will manage and mentor a skilled team, oversee platform evolution, and ensure technical excellence while institutionalizing data governance and operational procedures.

Qualifications

• 3+ years of experience managing or leading cross-functional data engineering teams.
• 10+ years of overall experience building scalable, high-quality data solutions.
• Experience implementing data quality frameworks and regulatory compliance (GDPR).

Responsibilities

• Lead and grow a high-performance team of data engineers.
• Architect and scale the Data Lakehouse and transformation frameworks.
• Drive the platform evolution and migration of legacy systems.

Skills

Data governance

Cloud data architecture

Data engineering development cycle

Stakeholder management

Education

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

Tools

AWS

Apache Iceberg

dbt

Airflow

Kafka

Job description

Position Overview

Reporting to the Director, Platform Engineering, we are searching for a Manager of Data Engineering to lead the development of our next-gen data ecosystem. The ideal candidate is a hands‑on builder who can lead a team of talented engineers through our platform evolution, enforce engineering standards, and bridge the gap between complex data architecture and business‑critical product needs. They also have a keen eye on continuously improving how 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, 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 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 contract 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, and Kafka.
• Strong Engineering Foundation: hands‑on experience in the 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.

Equal Employment Opportunity Statement

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.