Distinguished Engineer - Distinguished Engineer, Data Foundation

We’re hiring a Distinguished Engineer to own the technical vision for this entire domain: the data substrate (ingestion, storage, processing, and governance across the company) and the semantic data infrastructure that sits above it (ontologies, knowledge graphs, metrics layer, entity resolution, and the metadata/context services that power AI). This position expects you to exercise a company-wide technical authority position, not a people-management track. You’ll set direction for the data spin that spans every business unit (Consumer, Small Business, Credit Karma, Mailchimp) and every layer from the lakehouse to the LLM., * Scope of Impact

• Company-wide, not team-wide. Your architectural decisions become the default pattern for how Intuit ingests, models, governs, and serves data - across every product line and every region Intuit operates in.
• Two nested domains. (1) The data substrate: the core platform for data movement, storage, cataloging, quality, lineage, privacy/compliance, and observability that every downstream team builds on. (2) Semantic data infrastructure: the layer that gives that data meaning - canonical entities, business metric definitions, ontologies/knowledge graphs, and the retrieval/context infrastructure that feeds GenOS and other AI systems.
• Executive visibility. You’ll represent data and AI-infrastructure strategy in reviews with senior engineering and product executives, and act as an external technical ambassador (conferences, standards bodies, hiring, patents) when it advances Intuit’s reputation and talent brand.
• Force multiplier for the org. You’ll operate through influence as much as through code - setting technical strategy that dozens of teams and hundreds of engineers execute against, and personally stepping into the hardest, highest-leverage problems.
• What You’ll Do
• Define and evangelize the multi-year technical strategy for Intuit’s data platform and semantic infrastructure, aligned to company-wide AI and data ambitions.
• Architect the next generation of the data substrate: ingestion and streaming, lakehouse/warehouse design, data quality and observability, cataloging and lineage, and privacy-by-design controls that scale across business units.
• Design the semantic layer that unifies how Intuit’s systems and AI agents understand data - canonical entity models, metric definitions, ontologies and knowledge graphs, and the context/retrieval services that make that knowledge usable by GenOS and other agentic experiences.
• Resolve the hardest cross-cutting technical problems - the ones that cross team, org, or platform boundaries and have stalled without senior technical ownership.
• Set and enforce technical standards, reference architectures, and build-vs-buy decisions for data and semantic infrastructure company-wide; run architecture reviews for major initiatives.
• Partner with product, design, data science, AI/ML, privacy/legal, and Tech Ecosystem engineering leadership to translate business and regulatory requirements into durable technical designs.
• Mentor and raise the bar for Staff and Principal engineers across the data organization; help build the technical career ladder and craft-skill expectations for senior IC talent.
• Identify where the current data substrate or semantic layer will break under future scale, new regulation, or new AI workloads - and drive the roadmap to get ahead of it.

• 15+ years building large-scale data platforms, with a track record of designing systems that operate at Intuit-like scale (tens of millions of users, petabyte-class data, strict regulatory/compliance requirements).
• Deep, hands-on expertise across the modern data stack: distributed storage and compute (e.g., Spark, lakehouse formats), streaming (e.g., Kafka, Spark Streaming, Pulsar), orchestration, data governance/lineage/catalog tooling, and cloud data services (AWS or GCP).
• Demonstrated experience designing semantic or knowledge layers - metrics layers, ontologies, knowledge graphs, master/entity data management, or retrieval infrastructure for ML/LLM systems - and connecting them to real product or AI outcomes.
• A record of setting technical direction at company scale: prior work as a Distinguished Engineer, Fellow, Principal Architect, or equivalent, with artifacts (RFCs, ADRs, standards) that shaped how a large engineering organization builds.
• Fluency in data privacy and governance requirements (e.g., GDPR/CCPA-class regulation) and how they translate into architecture, not just policy.
• Strong understanding of how modern AI systems (RAG, agentic workflows, LLM context construction) depend on well-modeled, governed data - and what makes a semantic layer actually useful to them.
• Experience in designing agentic interfaces that can produce and govern a large knowledge base. Hands-on experience working with Deep learning approaches to handle large / sparse data problems.
• Experience in designing and building distributed data, and query systems with runtime optimizations.
• Exceptional written and verbal communication; the ability to move fluidly between a whiteboard architecture session with staff engineers and a strategy conversation with senior executives.
• A bias for personally shipping and de-risking the hardest problems, not just advising - this role still writes design docs, prototypes, and code where it matters most.

Preferred

• Experience in fintech, tax, accounting, or another highly regulated data-intensive domain.
• Prior ownership of a company-wide data platform or semantic/metrics layer used by multiple business units.
• Contributions to open-source data infrastructure, industry standards bodies, or published research/patents in data systems or knowledge representation.
• Experience building the metadata and context infrastructure behind a production generative-AI or agentic platform.

The expected base pay range for this position is: Mountain View $273,000 - $369,500 #J-18808-Ljbffr