Lead Data Infrastructure Engineer

About the role

Grab's Robotics Technology team, based in Singapore and China, is looking for a Lead Data Infrastructure Engineer to own the data backbone of its robotics programme. The fleet generates a continuous stream of sensor recordings, telemetry, autonomy events and operational records, and the engineer owns the pipelines, validation, monitoring dashboards and the integration of robotics data and ML workloads onto Grab's central data and ML platforms.

Key responsibilities

• Design, build and operate batch and streaming pipelines for robot telemetry, sensor recordings, autonomy events and operational data.
• Implement data quality and validation frameworks, including schema enforcement, completeness and freshness checks, anomaly detection and alerting.
• Build observability for pipelines (throughput, lag, cost, failures) and dashboards and curated datasets for engineering, data science and fleet operations.
• Integrate robotics data and ML workloads onto Grab's central lakehouse, orchestration, feature and model tooling.
• Design storage, indexing and retention for high-volume sensor data (ROS 2 / MCAP recordings, video, point clouds).
• Partner with autonomy, perception and ML engineers to make training and evaluation datasets reproducible and versioned.
• Own data access, governance, retention and compliance across regions, and reduce platform cost through partitioning, storage tiering and lifecycle policies.
• Document pipelines and datasets so users can self-serve.

Required profile

• Strong ownership, treating data correctness as a product commitment.
• Clear communicator who turns vague questions into concrete datasets or metrics.
• Collaborates across autonomy, ML, backend and operations teams.
• Comfortable inheriting and improving existing systems, and willing to be on-call.

Required skills

• Strong Python and SQL, with production-grade data services.
• Batch and streaming pipelines (Spark, Flink, Kafka or equivalents) and workflow orchestration (Airflow, Dagster, Argo Workflows or similar).
• Data warehouse and lakehouse experience (Iceberg, Delta, Hudi) and query engines (Presto/Trino, Spark SQL, BigQuery or equivalent).
• Observability and dashboarding (Prometheus/Grafana, Superset, Tableau or similar).
• Cloud engineering on Alibaba Cloud (Aliyun) and/or AWS, with Terraform as the primary IaC tool.
• Docker and Kubernetes.
• Nice to have: ROS 2 bags, Foxglove, Kubeflow/MLflow, CARLA and NVIDIA Isaac Sim, and experience across APAC sites.