Principal Engineer II (AI)

Location: On-site in Bellevue, Washington or Menlo Park, California, United States

Salary: $304K–$437K annually

Company

Snowflake builds a cloud data and AI platform for processing and governing enterprise data at scale.

What you will do

• Architect the core Data Transformation Engine, including stateful stream processing, incremental view maintenance, materialization internals, and distributed orchestration.
• Design processing primitives for Dynamic Tables, Streams, and Tasks that support real-time agentic AI workflows.
• Build low-level infrastructure for automated triggers, incremental processing, and native orchestration within the Snowflake engine.
• Develop reliable, multi-tenant systems that process exabytes of data with low-latency state propagation, transactional integrity, security, and resource isolation.
• Set the technical vision for data transformation capabilities and influence cross-product architecture, including Snowpark, Spark, Python, and Model Context Protocol workflows.
• Partner with strategic customers and field engineering teams while ensuring operational readiness, reliability, availability, and performance.

Requirements

• 14+ years of experience building database internals, distributed systems internals, or large-scale data processing engines.
• Deep expertise in stateful stream processing, incremental view maintenance, distributed transactions, and query execution internals.
• Proven experience solving consensus, replication, and high-concurrency problems at cloud scale.
• Strong technical leadership skills, including setting architectural direction, mentoring engineers, establishing standards, and aligning multiple teams.
• Understanding of traditional orchestration and data processing limitations, with the ability to design native engine-based solutions.
• Ability to collaborate across product and engineering boundaries in a globally distributed environment.

Culture & Benefits

• AI-native, experimental approach to solving complex engineering problems.
• Work on high-scale distributed systems powering enterprise data and AI applications.
• Cross-functional collaboration with engineering, product, field teams, and strategic customers.
• Responsibility for customer-facing reliability, availability, performance, confidentiality, and data security standards.