Data Platform Engineering Manager

About the role

Airwallex is hiring an Engineering Manager for its Realtime Data Platform, part of the Knowledge Platform team, which builds the foundational data and AI/ML infrastructure for the company. The role oversees large-scale, cross-functional initiatives and defines the multi-year vision and technical roadmap for real-time data analysis and serving needs, aligned with business and product goals. It also involves scaling engineering teams and setting up best practices for collaboration across sub-teams. The hiring location is Singapore.

Key responsibilities

• Provide visionary technical leadership and define a clear 1-3 year strategic roadmap for the Realtime Data Platform.
• Lead the multi-year effort to modernize the core data platform, introducing real-time analytical processing at petabyte scale.
• Partner with product teams to enable new data-driven features such as AI-powered applications or real-time dashboards.
• Scale and structure engineering teams, including hiring new talent and mentoring managers and senior individual contributors.
• Maintain strong relationships with product and other engineering teams and act as a trusted technical advisor on data and AI.

Required profile

• Minimum of 8 years of experience in data or software engineering, with at least 3 years in a leadership or management role.
• Proven experience managing and scaling an engineering team of 10+ people, including managers and senior individual contributors.
• Demonstrated ability to define and execute a technical strategy that delivered a significant business outcome or operational improvement.
• Preferred: experience within the financial domain.

Required skills

• Strong command of data and software engineering, with a focus on architecture and strategy.
• Deep understanding of distributed data processing (Apache Spark, Databricks) and event streaming (Kafka).
• Solid knowledge of modern data storage and serving technologies such as CubeJS, Elasticsearch and ClickHouse.
• Familiarity with observability tools such as Splunk, Grafana and Prometheus.
• Preferred: hands-on design of data processing patterns for a modern Lakehouse architecture, and ability to evaluate technologies and run proof of concepts.