Grab · api.smartrecruiters.com · checked today
Lead Data Infrastructure Engineer
Skills, with evidence
- Airflow / orchestration
Workflow orchestration at production scale (Airflow, Dagster, Argo Workflows or similar).
must have - Data quality
Demonstrated ownership of data quality: validation frameworks, data contracts, tests, and alerting on the data itself — not only on job success.
must have - Python
Strong Python and SQL, with the ability to write production-grade data services rather than one-off scripts.
must have - SQL
Strong Python and SQL, with the ability to write production-grade data services rather than one-off scripts.
must have - Spark
Hands-on experience building and operating both batch and streaming pipelines (Spark, Flink, Kafka or equivalents).
must have - Warehousing
Data warehouse and lakehouse experience: open table formats (Iceberg, Delta, Hudi), partitioning strategy, and query engines (Presto/Trino, Spark SQL, BigQuery or equivalent).
must have - AWS
Cloud engineering on Alibaba Cloud (Aliyun) and/or AWS, including object storage at scale, with Terraform as the primary IaC tool.
must have · not practised here - Kubernetes
Docker and Kubernetes for building and running data workloads.
must have · not practised here - Terraform
Cloud engineering on Alibaba Cloud (Aliyun) and/or AWS, including object storage at scale, with Terraform as the primary IaC tool.
must have · not practised here - Cost & performance
Build observability for the pipelines themselves: throughput, lag, cost, failure modes and clear on-call signals.
- Failure handling
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.
- Dashboards & BI
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.
not practised here - Streaming
Design, build and operate batch and streaming pipelines for robot telemetry, sensor recordings, autonomy events and operational data.
- Schema evolution & contracts
Demonstrated ownership of data quality: validation frameworks, data contracts, tests, and alerting on the data itself — not only on job success.
Your plan
- SQL
drills at the Staff level
- Python
drills at the Staff level
- Modelling
drills at the Staff level
- Pipelines
drills at the Staff level
- Spark
drills at the Staff level
The full plan, drill by drill, is on the job page.
leaves in 2 dremoved the moment Grab closes it