Principal Software Engineer

Quick Facts

• Role: Principal Software Engineer

• Focus: Technical architecture and delivery of lakehouse + context-graph platform components for AI workloads

• Reporting: Reports to the VP of Engineering

Description

You will own the technical architecture of core components across the data lake (Iceberg, Polaris, Trino) and the Context Engine. The role spans lakehouse table services and context graph ingestion, ontology-grounded retrieval/reasoning with LLMs, and driving the arc from early release to meaningful customer adoption. You will also set the bar for AI-native engineering and lead deep architecture reviews and cross-functional execution.

Responsibilities

• Lead platform architecture for data lake and Context Engine components

• Design and implement lakehouse table services and context graph ingestion with scalable, observable failure-recovery patterns

• Design how LLMs and enterprise ontologies work together (ontology-grounded retrieval/reasoning; LLM-assisted ontology construction, mapping, and evolution)

• Drive adoption feedback loops from early release to meaningful usage

• Raise the bar for AI in engineering itself (AI-assisted code review and test generation; AI agents as first-class users)

• Run deep architecture reviews, raise technical standards across the team, and represent the platform in technical/leadership forums

• Execute cross-functionally with Product, Design, and partner engineering to ship coherent experiences

Requirements

• 10+ years of software engineering experience shipping complex enterprise-grade data or SaaS products at scale

• Hands-on experience building production products from scratch with ownership of code and architecture decisions

• Deep cloud-native architecture experience (multi-tenant design, large-scale REST/gRPC APIs, Kubernetes, observability, multi-cloud)

• Expert distributed systems and event-driven architecture expertise with reliability, fault tolerance, and operational observability

• Strong data modeling across relational, graph, columnar, and streaming systems; experience with Iceberg and Trino

• Hands-on experience with Spark, Kafka, Iceberg, and Polaris for data platform capabilities

• Production-grade Python with hands-on services built with FastAPI

• Hands-on experience building LangChain or LangGraph applications and MCP servers for AI agents

• Hands-on production experience running LLMs (prompt/context design, structured outputs, evaluation, cost/latency trade-offs)

• Working knowledge of ontologies and semantic modeling (OWL, RDF, SKOS) for grounding LLM outputs

• Proficiency across the full engineering lifecycle, using AI daily to improve delivery velocity and software quality

• Technical influence without formal authority; proven track record driving architecture decisions across teams

Benefits

• Base pay guideline: $221,200 - $387,100, plus equity (when applicable), variable/incentive compensation, and benefits

• Health plans including flexible spending accounts

• 401(k) with company match and ESPP

• Matching donations, flexible time away plan, and family leave programs