snowflake
Prinicpal Software Engineer - Streaming Primitives
At a Glance
- Location
- US-WA-Bellevue, United States
- Employment
- FULL_TIME
- Experience
- 15+ years
- Compensation
- {'@type': 'MonetaryAmount', 'currency': 'USD', 'value': {'@type': 'QuantitativeValue', 'minValue': 264000, 'maxValue': 379500, 'unitText': 'YEAR'}}
- Department
- Snowflake
- Posted
- 2026-06-24
Key Requirements
Required Skills
Domain Knowledge
- Engineering
Requirements
15+ years of experience designing, building, and operating large-scale distributed data systems.
Deep expertise in at least one core area: stream processing, declarative query execution, pipeline orchestration, or data transformation at scale.
Proficiency in C++ or Java; comfort with systems-level reasoning (latency, throughput, resource efficiency at cloud scale).
Demonstrated ability to lead cross-team technical initiatives from blank-page architecture through production at petabyte scale across thousands of concurrent workloads.
Experience with a major analytical DBMS (Snowflake, BigQuery, Redshift, Databricks, Teradata).
Hands-on background in streaming or event-driven systems (Flink, Kafka, Spark Structured Streaming).
Responsibilities
Define and drive the technical direction for Snowflake's core data engineering and streaming transformation primitives, spanning Streams, Tasks, Dynamic Tables, and adjacent pipeline constructs.
Identify and lead multi-quarter technical investments — performance, scalability, correctness, and reliability — translating ambiguous problem spaces into concrete engineering plans with measurable outcomes.
Partner with product, research, and peer engineering teams to co-design primitives that compose cleanly across the data engineering stack.
Operate as a force multiplier: run architectural reviews, set the technical bar for design documents, and help engineers grow through high-quality feedback and sponsorship.
Work directly with customers and field teams to understand real-world usage patterns; use that signal to prioritize what matters next.
Contribute to Snowflake's technical reputation — through internal design influence, external talks, or research publications in the data engineering space.
Team
We build the core data engineering primitives that power Snowflake's streaming and transformation capabilities. From the constructs customers use to define real-time pipelines to the execution fabric that makes those pipelines reliable and cost-efficient at cloud scale, our team owns the full stack of declarative data engineering. We're a small, high-ownership team operating close to the product — which means your decisions ship, your architecture matters, and your fingerprints are on some of the most-used features in Snowflake's data engineering portfolio.