2 days ago
Singapore, SingaporeStaff+
Responsibilities
- Design, build, and operate batch and streaming pipelines for robotics telemetry, sensor recordings, autonomy events, and operational data.
- Implement data validation, schema enforcement, completeness and freshness checks, anomaly detection, and alerting.
- Build pipeline observability covering throughput, lag, cost, failures, and on-call signals.
- Create dashboards and curated self-serve datasets for engineering, data science, and fleet operations.
- Integrate robotics data and ML workloads with Grab’s central data and ML platforms.
- Design storage, indexing, and retention strategies for high-volume ROS 2, MCAP, video, and point-cloud data.
- Partner with autonomy, perception, and ML engineers to create reproducible and versioned training and evaluation datasets.
- Own data access, governance, retention, and compliance across operating regions.
- Reduce platform costs through partitioning, storage tiering, lifecycle policies, and compute right-sizing.
- Document pipelines and datasets for self-service use and participate in on-call support for data platform issues.
Requirements
- Strong Python and SQL skills with experience writing production-grade data services.
- Hands-on experience building and operating batch and streaming pipelines using Spark, Flink, Kafka, or equivalents.
- Production-scale workflow orchestration experience with Airflow, Dagster, Argo Workflows, or similar tools.
- Experience with data warehouses and lakehouses, open table formats, partitioning strategies, and query engines such as Presto/Trino, Spark SQL, or BigQuery.
- Demonstrated ownership of data quality through validation frameworks, data contracts, tests, and data-level alerting.
- Experience with observability and dashboarding tools such as Prometheus/Grafana, Superset, or Tableau.
- Cloud engineering experience with Alibaba Cloud and/or AWS, large-scale object storage, and Terraform.
- Experience using Docker and Kubernetes to build and run data workloads.
- Ability to evaluate storage tiering, egress, and compute trade-offs and act on cost implications.
- Strong ownership, communication, collaboration, prioritization, and ability to improve existing systems.
- Robotics or vehicle data experience, including ROS 2 bags, MCAP, time-series sensor data, or geospatial data, is preferred.
- Familiarity with Foxglove or similar robotics visualization tooling is preferred.
- ML platform experience with feature stores, training pipelines, experiment tracking, or model registries such as Kubeflow or MLflow is preferred.
- Large-scale multimedia data indexing and retrieval experience is preferred.
- Exposure to CARLA or NVIDIA Isaac Sim simulation-generated datasets is preferred.
- Experience with multi-region data infrastructure, cross-border data constraints, APAC sites, or distributed cross-border teams is preferred.
Benefits
- Term Life Insurance and comprehensive Medical Insurance.
- GrabFlex benefits package customization.
- Parental leave, Birthday leave, and Love-all-Serve-all volunteering leave.
- Confidential Grabber Assistance Programme.
- FlexWork arrangements with differentiated hours.
- Inclusive and equitable workplace policies.
Tech Stack
Alibaba CloudApache AirflowApache FlinkApache KafkaApache SparkApache SupersetAWSDockerGoogle BigQueryGrafanaKubernetesMLflowPrestoPrometheusPythonSQLTerraform
Categories
Data Engineering
About Grab
Grab builds a Southeast Asia superapp for mobility, deliveries, and financial services, serving consumers, drivers, merchants, and enterprises. It monetizes via ride and delivery commissions, merchant services (such as ads and subscriptions), and payments and digital banking products. Founded in 2012 and headquartered in Singapore, Grab operates across multiple Southeast Asian markets and is publicly listed on NASDAQ.