Get to know the Role
We are looking for a Data Infrastructure Engineer to own the data backbone of our robotics programme. Our fleet produces a large, continuous and unforgiving stream of data — sensor recordings, telemetry, autonomy events, and operational and delivery records — and almost every decision the team makes depends on that data arriving complete, on time and demonstrably correct. You will own the pipelines that move it, the validation and monitoring that prove it is trustworthy, the dashboards that engineering, operations and data science work from, and the integration of our robotics data and ML workloads onto Grab's existing data and ML platforms, so the team inherits Grab-scale tooling instead of rebuilding it. This is an ownership role over an established and growing footprint: you will take on existing pipelines, raise their reliability, and shape where the platform goes next.
The day-to-day activities
Design, build and operate batch and streaming pipelines for robot telemetry, sensor recordings, autonomy events and operational data.
Implement data quality and validation frameworks — schema enforcement, completeness and freshness checks, anomaly detection — with meaningful alerting when data breaks.
Build observability for the pipelines themselves: throughput, lag, cost, failure modes and clear on-call signals.
Build and maintain dashboards and curated, self-serve datasets for engineering, data science and fleet operations.
Integrate robotics data and ML workloads onto Grab's central data and ML platforms — lakehouse, orchestration, feature and model tooling — rather than maintaining parallel stacks.
Design storage, indexing and retention for high-volume sensor data (ROS 2 / MCAP recordings, video, point clouds), balancing accessibility against cost.
Partner with autonomy, perception and ML engineers to define metrics, and make training and evaluation datasets reproducible and versioned.
Own data access, governance, retention and compliance across the regions we operate in.
Drive down platform cost through partitioning, storage tiering, lifecycle policies and compute right-sizing.
Document pipelines and datasets so that consumers can self-serve without a hand-off conversation.
If you want your work measured by whether engineers and data scientists can trust and reach their data, and you enjoy making a data platform boringly reliable, we'd love to hear from you. Please apply with your resume and a brief note describing a data quality or pipeline reliability problem you owned: how you detected it, what you changed, and how you knew it stayed fixed.
Technical must-haves
Strong Python and SQL, with the ability to write production-grade data services rather than one-off scripts.
Hands-on experience building and operating both batch and streaming pipelines (Spark, Flink, Kafka or equivalents).
Workflow orchestration at production scale (Airflow, Dagster, Argo Workflows or similar).
Data warehouse and lakehouse experience: open table formats (Iceberg, Delta, Hudi), partitioning strategy, and query engines (Presto/Trino, Spark SQL, BigQuery or equivalent).
Demonstrated ownership of data quality: validation frameworks, data contracts, tests, and alerting on the data itself — not only on job success.
Experience with observability and dashboarding tooling (Prometheus/Grafana, Superset, Tableau or similar).
Cloud engineering on Alibaba Cloud (Aliyun) and/or AWS, including object storage at scale, with Terraform as the primary IaC tool.
Docker and Kubernetes for building and running data workloads.
Solid cost awareness: able to reason about storage tiering, egress and compute trade-offs and act on them.
Soft skills must-haves
Strong ownership; treats data correctness as a product commitment rather than a ticket queue.
Clear communicator who can turn a vague analytical question into a concrete dataset or metric definition.
Collaborative across autonomy, ML, backend and operations teams.
Pragmatic prioritization: knows when to ship a pipeline and when to fix the foundation underneath it.
Comfortable inheriting, understanding and improving existing systems, not only greenfield builds.
Willingness to be on-call for data platform issues.
Nice-to-haves
Experience with robotics or vehicle data: ROS 2 bags, MCAP, time-series sensor data and geospatial data.
Familiarity with visualization tooling for robotics data (Foxglove or similar).
ML platform experience: feature stores, training pipelines, experiment tracking and model registries (Kubeflow, MLflow or equivalent).
Experience with large-scale multimedia data (camera, lidar) indexing and retrieval.
Exposure to simulation-generated datasets — we primarily use CARLA and NVIDIA Isaac Sim.
Experience operating data infrastructure across multiple regions, including cross-border data constraints.
Experience working across APAC sites or with distributed, cross-border teams.
Life at Grab
We care about your well-being at Grab, here are some of the global benefits we offer:
We have your back with Term Life Insurance and comprehensive Medical Insurance.
With GrabFlex, create a benefits package that suits your needs and aspirations.
Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours
What We Stand For at Grab
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
