Lead Platform Engineer, Flink

Get to Know the Team

The Streaming Data team (a.k.a. Coban) ensures seamless and secure real-time access to continuous events or streams, serving as Grab's unified access pattern for real-time data. We build the infrastructure and platform for writing and consuming real-time data, and provide a cost-effective, managed NoOps service for product and data teams across Grab. We partner closely with sister teams in DataTech to provide integrated data platforms that unlock big data innovation every day.

Some examples of the team's work are shared publicly on the Grab Engineering blog:

These are examples of the platform thinking this role will continue to advance: taking complex real-time infrastructure problems and turning them into reliable, self-service capabilities for Grab teams.

Get to Know the Role

As a Lead Flink Platform Engineer, you will lead the design, evolution, and operation of Grab's stream processing platform, with Apache Flink as a core compute engine. You will drive medium to large projects across Data Engineering Platforms, mentor senior engineers, and be a technical go-to person for platform architecture, reliability, and production operations. The role is hands-on across Flink, Kafka, AWS cloud infrastructure, Kubernetes, observability, and SRE practices. You will work onsite at Grab Singapore office, One North, and report to the Senior Data Engineering Manager.

Why This Role Matters

• In an agentic world, high-quality real-time signals are becoming even more critical to drive automation, decision-making, and measurable business impact.
• Apache Flink is a critical real-time infrastructure layer for Grab. By making stream processing easier, safer, and more self-serve, this role helps unlock more real-time signals and turn data into business value across Grab. As Grab embraces agentic engineering, we welcome builders with strong data infrastructure experience and a passion for stream processing to join us.

The Critical Tasks You Will Perform

• Lead platform work that makes stream processing easy, reliable, efficient, and secure across Grab through self-serve capabilities built into the platform.
• Design and build abstractions, modules, and libraries that lower the barrier to adopting Flink and reduce operational and security toil for users.
• Improve automation and self-service workflows that allow a small platform team to support many production Flink pipelines at scale.
• Drive technical design discussions, production readiness reviews, incident learning, and long-term architecture improvements.
• Partner with Kafka, data lake, metrics, and data governance platform teams to make real-time data pipelines reliable across the broader Grab data ecosystem.
• Mentor engineers through design reviews, debugging sessions, code reviews, and operational best practices.