About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

The Context Intelligence team builds the data discovery and context serving foundations for Google Cloud. When enterprise organizations use artificial intelligence assistants to analyze their data, those assistants need clear context about what tables exist, how datasets relate to each other, and how data is used across the business. Our team solves this by transforming raw metadata into structured, searchable knowledge.

Working from our engineering hub in Warsaw, you will join a collaborative group of engineers who design distributed storage systems, graph search services, and data analysis pipelines. You will partner directly with teams building cloud databases, data governance tools, and conversational assistants to shape how enterprises interact with their data.

Responsibilities

• Design, build, and maintain distributed backend services that organize and deliver enterprise data context to AI agents across Google Cloud.
• Develop search, ranking, and graph traversal algorithms that identify relevant datasets, data relationships, and business definitions for user queries.
• Build data processing pipelines that analyze query history and usage patterns to automatically enrich metadata and filter out unused schema information.
• Create automated evaluation frameworks and testing tools to measure retrieval accuracy, response speed, and system efficiency.
• Collaborate with product managers and partner engineering teams across data analytics and artificial intelligence platforms to integrate new capabilities and support production operations.