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