Snowflake Senior Solution Engineer at Snowflake working with sales and channel partners to deliver data-driven demos and support enterprise Proof of Concepts. Focus on data, AI, and complex enterprise architecture.
Responsibilities
Present Snowflake's Data Cloud vision to data engineering leaders, analytics teams, data scientists, and executive stakeholders at prospects and customers
Lead hands-on technical discovery to map a customer's existing data architecture — pipelines, warehouses, lakehouses, governance gaps — and design a Snowflake-native path forward
Build and deliver tailored demos and proof of concepts across Snowflake's full data platform: data engineering (Snowpark, Dynamic Tables, Snowpipe), AI/ML (Cortex AI, ML model registry), and data sharing (Data Clean Rooms, Marketplace listings)
Guide customers through modern data architecture patterns including Data Mesh, Data Lakehouse, real-time streaming pipelines, and unified governance frameworks
Translate ambiguous business problems — churn prediction, supply chain visibility, financial consolidation, operational analytics — into concrete Snowflake data solutions
Partner with Product Management, Engineering, and Field teams to feed customer data architecture patterns back into the product roadmap
ON DAY ONE WE WILL EXPECT YOU TO HAVE:
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; able to write working code in a customer session without hesitation
Broad experience across the modern data stack: ETL/ELT tools (dbt, Fivetran, Informatica, Spark), streaming platforms (Kafka, Kinesis), orchestration (Airflow, dbt Cloud), BI tools (Tableau, Looker, Power BI), and cloud infrastructure (AWS, Azure, GCP)
Proven ability to conduct deep data architecture discovery — understanding a customer's source systems, transformation layers, consumption patterns, and governance requirements — and connect those findings to a Snowflake reference architecture
Experience positioning AI and ML capabilities within a data platform context: feature stores, model training pipelines, LLM-powered applications, and AI governance
Qualification
Deep hands-on expertise with SQL
Required
3–8 years of industry experience in pre-sales or solutions architecture focused on data platforms
Deep hands-on expertise with SQL, Python, and cloud data warehouse / data lakehouse architectures
