Distinguished Engineer - Distinguished Engineer, Data Foundation

Mountain View, CA · Distinguished

$273,000 – $369,500

Listed on Intuit’s own careers site. You apply with them directly — we never stand between you and the employer.

What this role is

Intuit is seeking a Distinguished Engineer to architect and govern the company's data platform and semantic infrastructure across all business units, from raw data ingestion through governance to AI-ready knowledge layers. This role suits someone with 15+ years building massive-scale data systems who can set technical direction across dozens of teams and personally tackle the highest-leverage problems at the intersection of data, AI, and compliance.

Our summary, not Intuit’s wording. The full posting is on their site.

Skills this role names

What they ask for

Required

15+ years building large-scale data platforms at tens-of-millions-user scale
Deep expertise across modern data stack: distributed storage, compute, streaming, orchestration, governance, cataloging, cloud data services
Demonstrated experience designing semantic layers, metrics layers, knowledge graphs, or entity data management
Track record setting technical direction at company scale with architecture artifacts
Fluency in data privacy and governance (GDPR/CCPA-class regulation)
Understanding of how modern AI systems depend on well-modeled, governed data
Experience designing distributed data and query systems with runtime optimizations
Exceptional written and verbal communication skills
Bias toward personally shipping and de-risking the hardest problems

Nice to have

Experience in fintech, tax, accounting, or other highly regulated data-intensive domain
Prior ownership of a company-wide data platform or semantic layer used by multiple business units
Contributions to open-source data infrastructure, industry standards bodies, or published research in data systems
Experience building metadata and context infrastructure for production generative-AI or agentic platforms
Experience with agentic interfaces and deep learning for large/sparse data problems