About the job
GrabX serves as Grab's essential Configuration Management and Experimentation platform, designed to empower product teams to safely deploy features and leverage data-driven insights through A/B testing and automated experiment analysis. Handling the immense scale of Grab’s traffic, it manages real-time configurations for millions of concurrent users while processing petabytes of data to deliver automated, statistically robust insights. Our mission is to shorten the 'time to insight' for engineers and product managers, ensuring that every new feature at Grab is underpinned by high-fidelity data.
Role Overview
We are seeking a Lead Data Engineer to join our GrabX team. This pivotal role emphasizes the data infrastructure for experimentation, leading the design of high-throughput data pipelines that support our automated analysis systems. You will report directly to an Engineering Manager based in Singapore and work onsite at the Grab Vietnam office located in District 7, HCMC.
Technical Stack
Data Processing & Storage: Apache Flink, Apache Spark, Trino, StarRocks, and Delta Lake.
Online Serving & Configuration: Golang, AWS infrastructure, Redis, ScyllaDB, and DynamoDB.
Infrastructure: Kubernetes, Terraform, and CI/CD for data workloads.
Key Responsibilities
Design and construct resilient batch and real-time data pipelines to process billions of experiment events daily for automated analysis.
Create highly efficient Spark or Flink jobs to compute complex business metrics and statistical significance at scale.
Develop comprehensive data validation and monitoring frameworks to guarantee the accuracy and reliability of experiment results.
Lead the evolution of our data architecture towards a centralized Metric Store model to facilitate seamless reuse across product teams.
Mentor a team of engineers while promoting best practices in data modeling and system design for data-intensive applications.
Collaborate with Data Scientists to automate hypothesis testing and anomaly detection within the experimentation lifecycle.

