Product Marketing Engineer (Analytics)

TL;DR

Product Marketing Engineer (Analytics): Leading competitive benchmarking for Snowflake’s Analytics portfolio by designing technical strategies and executing performance workloads with an accent on data warehouse engine performance and competitive differentiation. Focus on translating complex technical benchmark results into market-facing narratives and providing insights to inform product roadmaps.

Location: Hybrid: Must be based in Menlo Park (CA), Dublin (CA), or Bellevue (WA)

Compensation: $165K – $216.3K

Company

Snowflake is a cloud data platform powering the era of the agentic enterprise through AI-native thinking and high-performance data solutions.

What you will do

• Develop and own the technical benchmarking strategy for the Analytics portfolio.
• Design rigorous benchmarking methodologies, workload definitions, success criteria, and validation processes.
• Configure environments, execute benchmark tests, and validate findings with engineering and product stakeholders.
• Translate complex technical findings into clear customer outcomes and differentiated product messaging.
• Create technical content for external audiences, including blogs, benchmark reports, presentations, and demos.
• Monitor competitive analytics products and develop strategic responses to competitive technical claims.

Requirements

• 6+ years of experience in product engineering, performance engineering, technical product marketing, or data engineering.
• Strong understanding of modern data platforms, cloud data warehouses, and BI architectures.
• Hands-on experience designing and executing technical benchmarks and performance tests.
• Proficiency in SQL, data modeling, and query performance analysis for large-scale analytical systems.
• Exceptional written and verbal communication skills to simplify complex concepts for external audiences.
• BS/BA in Computer Science, Engineering, Information Systems, or equivalent practical experience.

Nice to have

• Experience benchmarking platforms such as Databricks, Google BigQuery, Amazon Redshift, or Microsoft Fabric.
• Familiarity with semantic layers, metrics stores, ontologies, and real-time analytics.
• Experience with performance testing tools, infrastructure-as-code, and cloud environments.
• Ability to produce technical videos, live demonstrations, or conference presentations.
• Understanding of competitive intelligence practices.

Culture & Benefits

• Experimental mindset focused on rapid testing of emerging capabilities.
• Low-ego, dynamic, and fast-moving work environment.
• Opportunity to help redefine the future of how work gets done in an AI-driven enterprise.