Distinguished Engineer - Data Foundation

Company Overview

Job Overview|Job Description

Intuit runs on data — every TurboTax return, every QuickBooks ledger, every Credit Karma recommendation, and every Mailchimp campaign draws on a common substrate of pipelines, stores, and governance that has to be fast, trustworthy, and compliant at the scale of tens of millions of customers. Increasingly, that substrate also has to be legible to machines: Intuit's GenOS and agentic AI experiences depend on a semantic layer that turns raw data into governed metrics, entities, and domain knowledge (tax code, accounting rules, cash-flow concepts) that models and applications can reason over.

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.

Responsibilities

• 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.

Qualifications

• 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.

• 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.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender. 

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

Qualifications|What you'll bring

• 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.