Snowflake
Senior Solution Engineer
US-NC-Remote. Posted 18 days ago
The role
You will work with sales teams and channel partners to understand customer data challenges and design Snowflake-native solutions. You will deliver demonstrations, lead technical discovery, and support enterprise Proof of Concepts.
What you will do
- Present Snowflake's Data Cloud vision to stakeholders
- Lead hands-on technical discovery to map customer data architecture
- Build and deliver tailored demos and proof of concepts
- Guide customers through modern data architecture patterns
- Translate business problems into concrete Snowflake data solutions
- Partner with Product Management, Engineering, and Field teams
Required qualifications
- 3–8 years of industry experience in a pre-sales or solutions architecture role focused on data platforms
- Deep hands-on expertise with SQL, Python, and cloud data warehouse / data lakehouse architectures
- Broad experience across the modern data stack including ETL/ELT tools, streaming platforms, orchestration, BI tools, and cloud infrastructure
- Proven ability to conduct deep data architecture discovery
- Experience positioning AI and ML capabilities within a data platform context
- Strong intuition for data governance, quality, and compliance challenges
- Ability to quantify the business value of a modern data architecture
- Demonstrated track record using AI code generation tools to accelerate prototyping
- University degree in computer science, data science, engineering, mathematics or a related field, or equivalent practical experience
Nice to have
- Master's in Data Science or Business Analytics
- Experience working with GSIs (EY, Deloitte, Accenture, Slalom, etc.) on large data platform programs
Benefits
- Base salary
- Variable compensation
- Equity
- 401(k)
- health/dental/vision
- flexible PTO
- professional development budget
About the company
Snowflake makes modern data warehousing effective, affordable, and accessible. The Snowflake Data Cloud is a single governed platform for building data pipelines, running ML workloads, sharing live data, and powering AI applications.