The Insights organization within Google Core builds and operates the foundational data analysis platforms and languages used across the entire company. Our portfolio covers a wide range of deep, complex technical topics that affect every part of Google, including all of Google's primary business intelligence and exploratory analysis platform, GoogleSQL, and the core services and libraries that power Google's largest data processing systems. We are focused on driving the technical strategy for how Google uses agentic infrastructure to drive analysis, with the potential to effect transformative change across almost every part of Google—from pure data analysts and GDM to engineers, POps, and executives.
As a Principal Engineer for the newly formed Insights pillar, you will serve as the organization’s primary technical authority for agentic data analysis. Partnering with the Director and other senior technical leaders across Core, Cloud, and GDM, you will shape the long-term technical vision and strategy for how Google uses agentic infrastructure to drive analysis and insight generation.
In this role, you will provide technical guidance across the Insights organization and lead the development of agentic tools and foundational technologies for Google-wide languages, APIs, and systems. You will act as the technical connective tissue across Google, bridging the gap between internal specialized systems and external standards (e.g., standard ANSI SQL).
The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $307000 - $428000 (USD) + 30% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Redefine data analysis architecture. Design a shared component ecosystem (skills, sub-agents, RAG systems, embeddable UIs) across harnesses (Plx, Cloud, Jetski).
- Guide Core, Cloud, and GDM teams. Resolve complex disputes and ensure enterprise-wide coherence, bridging internal systems with external cloud standards.
- Act as Insights' unifying technical liaison, collaborating with VPs and senior ICs to establish common infrastructure.
- Bridge technical gaps, specifically translating cloud-generated ANSI SQL into the specialized Google SQL required internally.
- Align senior ICs and disparate in-flight efforts to work on the highest-impact data analysis problems. Mentor staff/senior staff engineers. Lead the cultural transition from traditional software engineering to AI-orchestrated development models.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science or a related technical field or equivalent practical experience.
- 15 years of experience in software engineering.
- Experience designing, building, and maintaining large-scale distributed systems.
- Experience serving as a senior technical leader (e.g., principal engineer, senior staff engineer, or equivalent) overseeing portfolios and multiple engineering teams.
- Experience in architectural design and systems integration for multi-stakeholder, enterprise-grade platforms.
Preferred qualifications:
- Master’s degree or PhD in Computer Science, Machine Learning, Computer Engineering, or a related highly technical field.
- Experience with modern data technologies, distributed computing frameworks, or open-source data lakehouse standards (e.g., Iceberg, Spark, or equivalent).
- Experience building high-quality, compelling developer tools or standardized component ecosystems that teams voluntarily adopt.
- Knowledge of agentic harnesses, large language models, agentic AI workflows (such as sub-agent configuration, skills, and RAG systems), and developer ecosystems.
- Extensive technical domain expertise in large-scale data analysis, database systems, or Business Intelligence (BI).
- Proven track record of working with large-scale, unstructured, and "messy" data ecosystems at scale.