# Principal Software Engineer - Data Engineering & Streaming Primitives

**Company:** [Snowflake](https://hotfix.jobs/companies/snowflake)
**Location:** Bellevue, WA
**Role:** Data Engineering
**Salary:** $264k – $380k/yr
**Experience:** 15+ years
**Skills:** C++, Java, Distributed Systems, Stream Processing, Data Transformation, Fault Tolerance, Consistency Models, Pipeline Orchestration, Declarative Query Execution, Petabyte Scale Systems
**Posted:** 2026-06-24

> Principal-level engineer to define and lead Snowflake's core data engineering and streaming primitives (Streams, Tasks, Dynamic Tables) at cloud scale. Requires 15+ years building large-scale distributed data systems and deep expertise in stream processing or data transformation.

## Job Description

## What You'll Do
- 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.

## What We're Looking For
- 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.
- Strong computer science fundamentals — distributed systems, algorithms, fault tolerance, and consistency models.
- 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.
- Strong written and verbal communication skills; ability to represent complex technical trade-offs clearly to engineering, product, and leadership audiences.

## Nice to Have
- 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).
- Familiarity with the broader data engineering ecosystem: dbt, Airflow, Fivetran, Iceberg, Delta Lake.
- Experience with CDC, change propagation, or incremental computation patterns.
- Advanced degree (MS or PhD) in Computer Science, with emphasis on database or distributed systems.

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