Senior Solution Engineer

In This Role You Will Get To:

• 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:

• 7–8 years of industry experience, with a minimum of 5 years 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

• Strong intuition for data governance, quality, and compliance challenges (GDPR, CCPA, PCI) and how platform design decisions affect them

• Ability to quantify the business value of a modern data architecture — cost reduction, time-to-insight, pipeline reliability, data product monetization

• A demonstrated track record using AI code generation tools to accelerate prototyping and proof-of-concept delivery

• University degree in computer science, data science, engineering, mathematics or a related field, or equivalent practical experience. A Master's in Data Science or Business Analytics is a plus

• Experience working with GSIs (EY, Deloitte, Accenture, Slalom, etc.) on large data platform programs is beneficial but not required