Kerja Sepenuh Masa, Lead Product Manager, Data Platform (Data Mesh) di Grab - Maukerja

Get to Know the Team

The Data Platform team is a collective of software engineers, product managers, product designers, and technical program managers, who build the data foundation that powers Grab's business. Platforms within the team's scope encompass Grab's Data Lake and Signals Marketplace, Data Discovery and Catalog, Customer Data Platform, Experimentation, Metrics, and others. Our mission is to turn data into a growth engine within the Grab ecosystem and to enable Grabbers to work smarter, faster, and more collaboratively.

The Data Mesh team owns Signals Marketplace, Grab's data mesh. Domain teams own their data, and we give them the platform and shared standards to model, test and secure it. We also run the catalog where analysts, data scientists and ML engineers find that data every day, increasingly through AI agents.

Get to Know the Role

We have certified our most-used data assets and made them easy to find. The opportunity now is to get full value from this data by moving consumers onto it and off the long tail of tables they use today. We want a data PM who has built a data mesh or a shared enterprise data model and can use that experience to shape Grab's Data Mesh. Your job is to get every team and agent at Grab building on a consistent model of the business.

You will set the standards every producing team at Grab follows, and keep them light wherever the stakes are low. You will own the AI tools that data engineers and analysts use to build, test and query data, and drive their adoption across Grab.

This is an individual contributor role based onsite in Grab's headquarters in Singapore, reporting to the Head of Product, Data and AI Platforms.

The Critical Tasks You Will Perform

• You will own the strategy and roadmap for Grab's data mesh, and be responsible for data throughput over modeled data.
• You will own how Grab models its core business entities, how they map to physical data, and how they relate to each other.
• You will make modeled data products the default for analysis and for training Grab's AI models. Publishing one must take a domain team less work than publishing a raw table, and break the tradeoff with data duplication.
• You will own the data quality testing, with stricter tests for the data that matters most.
• You will decide which data Grab cannot afford to get wrong, by what breaks when it fails and what growth it drives. Consumers of that data provide inputs for the contracts that producer changes must pass.
• You will ensure Grab's data mesh is secure with proper access control and data classification.
• You will own Grab's data catalog, including lineage and the business context behind each table, and automate its upkeep.
• You will build in the open and do your own analysis through our agents.

What Essential Skills You Will Need

• Product Management: 6+ years in technical product management, including 2+ owning data platforms. Internal platforms and commercial products both count.
• Data Mesh and Data Modeling: You have made shared definitions stick across teams that each own their data, using data mesh, a layered lakehouse, or entity and semantic modeling.
• Data Architecture: You can trace data from Kafka through Spark or Flink jobs into the Iceberg tables analysts query in Trino, and spot where an upstream change quietly breaks a consumer even when the schema holds. You use that to challenge engineering designs.
• Data Trust: You have shipped data contracts, quality checks or incident workflows, tiered by business impact so higher tiers get stricter tests and faster fixes.
• Governance and Security: You have built access control or data classification into a data platform, for example tag-based policies or column masking. You can explain how an attacker would use an AI agent against it, and how you would stop them.
• AI and Agents: You have shipped metadata or context that agents read from DataHub or a similar catalog, and shown with evals that their answers got more accurate. You build with AI coding agents yourself.
• Influence: You have moved teams or customers you do not control onto a standard you set, and retired what it replaced.