Senior Product Manager, Data Commons Infrastructure
Summary
Own the product roadmap for Google’s global data ingestion platform, defining schema models and overseeing ETL pipelines that power the Data Commons Knowledge Graph.
At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.
In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.
One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.
We are seeking a technical, AI savvy Product Manager to own strategy, architecture and execution of our data ingestion platform in Bangalore, India. This role centers on our core data import flow and processing pipeline, which powers the global Data Commons Knowledge Graph. You will define the product roadmap for data ingestion tooling, manage the end-to-end import of the public datasets, oversee data processing, be responsible for data quality and shape the data models & ontologies that structure our graph.
You will support our team, you will bridge ambitious long-term technical direction (e.g. scaling ingestion throughput 100x) with crisp 6-week execution milestones. You will build and foster a cohesive, highly collaborative team culture in Bangalore, operating with high autonomy, driving consensus, over-communicating across time zones with the US.
- Develop a scalable, potentially AI driven product strategy and roadmap for the end-to-end data ingestion pipeline. Take pipeline responsibilities which include sourcing, landing, schematizing, processing, entity resolution, and storing data in our knowledge graph.
- Balance ambitious, long-term platform scaling goals (100x ingestion scaling) with pragmatic, near-term execution and clear 6-week deliverables.
- Influence and design essential schema models and ontologies ("semi-ontologist") required to represent complex public datasets cleanly across knowledge graph and relational schemas.
- Own product requirements for ingestion tools, schema mapping UIs, automated processors, Extract Transform Load (ETL) pipelines, Data Quality, Observability, and Monitoring.
- Oversee vendor partnerships with precision, clearly demarcating internal core ownership vs. vendor-managed pipeline tasks, establishing clear accountability, and optimizing incentive alignment.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in product management or a related technical role.
- 5 years of experience with direct focus on data pipelines, Extract, Transform, and Load (ETL) infrastructure, or backend data systems.
- Experience in data modeling, schema design, and API-first development.
Preferred qualifications:
- Experience with Knowledge Graphs, ontologies, ontologies for statistics, semantic formats (JSON-LD), and entity resolution frameworks.
- Experience managing vendor engagements, defining clear boundaries of ownership, and driving partner accountability.
- Experience in data engineering concepts: ETL, Data Quality, Observability/Monitoring, DataProc, and database systems (BigQuery, Graph databases, SQL).
- Ability to leverage product tools, engage with the technical stack, and operate as an Infrastructure Product Manager (PM).
- Exceptional cross-site communication skills.
- Familiarity with AI/ML platforms, agentic systems, evaluation systems (and associated stats), and agentic loop architectures for automated data extraction, schematization, or validation.