Principal Software Engineer - Data Engineering & Streaming Primitives

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.