{"id":3370761,"url":"https://alion.io/job/grab-lead-data-infrastructure-engineer","title":"Lead Data Infrastructure Engineer","company":{"id":158,"name":"Grab","domain":"grab.com","url":"https://alion.io/company/grab","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":54,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-11-03T05:54:40Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":56000,"max_usd":128000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":771},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Argo Workflows","optional":false},{"name":"AWS","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"Flink","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Grafana","optional":false},{"name":"Kubernetes","optional":false},{"name":"Presto","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"ROS","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Superset","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"Trino","optional":false},{"name":"CARLA","optional":true},{"name":"Embodied AI","optional":true},{"name":"Feature Store","optional":true},{"name":"Foxglove Studio","optional":true},{"name":"Isaac Sim","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"Time Series","optional":true}],"status":"live","first_seen_at":"2026-10-27T06:54:03Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-11-03T16:31:45Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"About Grab and Our Workplace\nGrab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.\n About the Team:\nThe Robotics Technology team is a core part of Grab's long-term vision to build urban embodied AI. Our engineers take full ownership of the product lifecycle: designing and manufacturing hardware in-house, developing control and machine-learning systems, and rigorously testing in real-world conditions and production fleet operations. This is a fast-moving, multidisciplinary environment where software, hardware and data science experts collaborate closely to solve practical challenges at scale. We are executing an ambitious growth plan to expand our robotics fleet across cities over the coming years, and we are focused on delivering highly productive, safe and efficient robot delivery services that help address current delivery labor shortages.\nBased in Singapore and China, our team offers opportunities to work on cutting-edge autonomy, deploy solutions in complex urban environments, and directly influence the future of last-mile logistics. If you're excited by tangible impact, large-scale systems and cross-functional engineering, you'll find meaningful challenges and rapid career growth here.\nGet to know the Role:\nWe 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.\nThe day-to-day activities\nDesign, build and operate batch and streaming pipelines for robot telemetry, sensor recordings, autonomy events and operational data.\n\nImplement data quality and validation frameworks - schema enforcement, completeness and freshness checks, anomaly detection - with meaningful alerting when data breaks.\n\nBuild observability for the pipelines themselves: throughput, lag, cost, failure modes and clear on-call signals.\n\nBuild and maintain dashboards and curated, self-serve datasets for engineering, data science and fleet operations.\n\nIntegrate robotics data and ML workloads onto Grab's central data and ML platforms - lakehouse, orchestration, feature and model tooling - rather than maintaining parallel stacks.\n\nDesign storage, indexing and retention for high-volume sensor data (ROS 2 / MCAP recordings, video, point clouds), balancing accessibility against cost.\n\nPartner with autonomy, perception and ML engineers to define metrics, and make training and evaluation datasets reproducible and versioned.\n\nOwn data access, governance, retention and compliance across the regions we operate in.\n\nDrive down platform cost through partitioning, storage tiering, lifecycle policies and compute right-sizing.\n\nDocument pipelines and datasets so that consumers can self-serve without a hand-off conversation.\n\nIf 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.\n Technical must-haves\nStrong Python and SQL, with the ability to write production-grade data services rather than one-off scripts.\n\nHands-on experience building and operating both batch and streaming pipelines (Spark, Flink, Kafka or equivalents).\n\nWorkflow orchestration at production scale (Airflow, Dagster, Argo Workflows or similar).\n\nData warehouse and lakehouse experience: open table formats (Iceberg, Delta, Hudi), partitioning strategy, and query engines (Presto/Trino, Spark SQL, BigQuery or equivalent).\n\nDemonstrated ownership of data quality: validation frameworks, data contracts, tests, and alerting on the data itself - not only on job success.\n\nExperience with observability and dashboarding tooling (Prometheus/Grafana, Superset, Tableau or similar).\n\nCloud engineering on Alibaba Cloud (Aliyun) and/or AWS, including object storage at scale, with Terraform as the primary IaC tool.\n\nDocker and Kubernetes for building and running data workloads.\n\nSolid cost awareness: able to reason about storage tiering, egress and compute trade-offs and act on them.\n\nSoft skills must-haves\nStrong ownership; treats data correctness as a product commitment rather than a ticket queue.\n\nClear communicator who can turn a vague analytical question into a concrete dataset or metric definition.\n\nCollaborative across autonomy, ML, backend and operations teams.\n\nPragmatic prioritization: knows when to ship a pipeline and when to fix the foundation underneath it.\n\nComfortable inheriting, understanding and improving existing systems, not only greenfield builds.\n\nWillingness to be on-call for data platform issues.\n\nNice-to-haves\nExperience with robotics or vehicle data: ROS 2 bags, MCAP, time-series sensor data and geospatial data.\n\nFamiliarity with visualization tooling for robotics data (Foxglove or similar).\n\nML platform experience: feature stores, training pipelines, experiment tracking and model registries (Kubeflow, MLflow or equivalent).\n\nExperience with large-scale multimedia data (camera, lidar) indexing and retrieval.\n\nExposure to simulation-generated datasets - we primarily use CARLA and NVIDIA Isaac Sim.\n\nExperience operating data infrastructure across multiple regions, including cross-border data constraints.\n\nExperience working across APAC sites or with distributed, cross-border teams.\n\n Life at Grab\nWe care about your well-being at Grab, here are some of the global benefits we offer:\nWe have your back with Term Life Insurance and comprehensive Medical Insurance.\nWith GrabFlex, create a benefits package that suits your needs and aspirations.\nCelebrate 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\nWe have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.\nBalancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours\nWhat We Stand For at Grab\nWe are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. 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