Principal Software Engineer II – Data Platform (Streaming & Dynamic Tables)

About the role

At Snowflake we are powering the era of the agentic enterprise, seeking AI‑native thinkers who treat AI as a high‑trust collaborator. As a Principal Engineer on the Data Platform team you will be a technical anchor—setting direction, solving the hardest engineering problems, and elevating the entire team. You will work at the intersection of systems design, long‑term architecture, and real‑world engineering execution.

Key responsibilities

• Drive the technical strategy and architecture for key Data Platform initiatives, focusing heavily on real‑time data movement and incremental processing.
• Lead design reviews and set engineering standards across teams.
• Identify and resolve high‑impact technical challenges and systemic bottlenecks to reduce latency and improve the efficiency of dynamic tables at cloud scale.
• Mentor and grow senior engineers, raising the engineering bar.
• Partner closely with product and infrastructure leadership on roadmap and direction.
• Deliver continuous hands‑on technical solutions in the most critical areas.

Required profile

• 14+ years of software engineering experience with deep expertise in distributed systems.
• Proven track record of defining and delivering platform‑scale technical initiatives.
• Expert‑level knowledge of streaming and/or batch data systems (Flink, Spark, or equivalent) with specialized knowledge in incremental materialization algorithms.
• Strong systems thinking ability to reason across infra, storage, compute, and API layers.
• Passion for engaging directly with customers to gain insight and inspiration.
• Experience influencing technical direction across organizations.
• Excellent written and verbal communication for technical and executive audiences.

Required skills

• Flink
• Spark
• Distributed systems
• Incremental materialization algorithms
• Cloud‑native data systems
• Metadata systems
• Query optimization
• Data catalog infrastructure
• AI‑augmented development practices
• LLM‑based tooling