Senior Data Engineer, GrabX (Experimentation & Config Management)

GrabX is Grab's mission-critical Configuration Management and Experimentation platform. We empower every product team at Grab to ship features safely and make data-driven decisions through A/B testing and automated experiment analysis. The platform handles the massive scale of Grab's traffic, managing real-time configurations for millions of concurrent users and processing petabytes of data to provide automated, statistically rigorous insights. Our goal is to minimize the "time to insight" for engineers and product managers, ensuring that every feature launched at Grab is backed by high-fidelity data.

Get to Know the Role

We are looking for a Senior Data Engineer for the GrabX team. This position focuses heavily on the data backbone of experimentation, leading the design of high-throughput data pipelines that feed our automated analysis engines. You will report to an Engineering Manager in Singapore and work onsite at the Grab Vietnam office (District 7, HCMC).

The technical stack includes:

• Data Processing & Storage: Apache Flink, Apache Spark, Trino, StarRocks, and Delta Lake.
• Online Serving & Config: Golang, AWS infrastructure, Redis, ScyllaDB, and DynamoDB.
• Infrastructure: Kubernetes, Terraform, and CI/CD for data workloads.

The Critical Tasks You Will Perform

• Design and build robust batch and real-time data pipelines to process billions of experiment events daily for automated analysis.
• Develop highly efficient Spark or Flink jobs to calculate complex business metrics and statistical significance at scale.
• Implement rigorous data validation and monitoring frameworks to ensure experiment results are accurate and trustworthy.
• Lead the transition of our data architecture toward a centralized Metric Store model for seamless reuse across product groups.
• Mentor a team of engineers while driving best practices in data modeling and system design for data-heavy applications.
• Collaborate with Data Scientists to automate hypothesis testing and anomaly detection within the experimentation lifecycle.