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Salary: USD 220,000 - 288,000 / annual
Snowflake is seeking a Senior Solution Engineer to drive enterprise data platform sales cycles by combining deep technical expertise with customer-centric problem-solving. You will work directly with sales teams and channel partners to understand customer data challenges, design Snowflake-native solutions, and deliver compelling proof-of-concepts that demonstrate measurable business outcomes.
In this role, you will lead hands-on technical discovery sessions to map existing data architectures—pipelines, warehouses, lakehouses, governance—and architect modern data solutions. You'll build and deliver tailored demonstrations across Snowflake's full platform: data engineering (Snowpark, Dynamic Tables, Snowpipe), AI/ML (Cortex AI, model registry), and data sharing capabilities. You'll guide customers through modern patterns including Data Mesh, Data Lakehouse, real-time streaming, and unified governance.
You will translate ambiguous business problems—churn prediction, supply chain visibility, financial consolidation—into concrete Snowflake solutions. You're equally comfortable whiteboarding medallion architectures with data engineering teams and presenting ROI to C-suite executives. You'll partner with Product, Engineering, and Field teams to feed customer insights back into the product roadmap.
Required: 7–8 years industry experience with minimum 5 years in pre-sales or solutions architecture for data platforms. Deep hands-on expertise in SQL, Python, and cloud data warehouse/lakehouse architectures. Broad experience across the modern data stack: ETL/ELT (dbt, Fivetran, Informatica, Spark), streaming (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 and connect findings to reference architectures. Experience positioning AI/ML capabilities: feature stores, model training, LLM applications, AI governance. Strong intuition for data governance, quality, and compliance (GDPR, CCPA, PCI). Ability to quantify business value of modern data architecture. Demonstrated track record using AI code generation tools. University degree in computer science, data science, engineering, mathematics, or equivalent practical experience; Master's in Data Science or Business Analytics is a plus. Experience with GSIs (EY, Deloitte, Accenture, Slalom) on large data platform programs is beneficial.
