Present Snowflake’s technology and vision to executives and technical contributors at prospects and customers
Work hands-on with prospects and customers to demonstrate and communicate the value of Snowflake technology throughout the sales cycle, from demo to proof of concept to design and implementation
Create and develop technical champions in your accounts to drive deals and achieve a technical win
Be at the cutting edge of Snowflake technology and confidently present Snowflake roadmap features and functionality to customers and/or prospects
Immerse and enable yourself in the ever-evolving industry, maintaining a deep understanding of competitive and complementary technologies and vendors and how to position Snowflake in relation to them
Work closely with other sales engineers to make each other the best and constantly learn from wins and losses
Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing
Represent Snowflake at industry or customer events
Work with our ecosystem and implementation partners to build joint architectures or collaborate on account strategies and initiatives to help our customers be successful
On day one you we will expect you to have:
Sales engineering/solution architect experience in a Saas environment or relevant industry experience (analytics, data science, data engineering etc)
Outstanding presentation skills to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos
Understanding of and experience with data architecture, data analytics and cloud technology
Hands on experience with SQL
Ability to solve customer specific business problems and apply Snowflake’s solutions
Customer-facing skills to effectively communicate our vision to a wide variety of technical and executive audiences both written and verbal
Fluency in German language - written & spoken
Preferred (but not required) to have:
Hands on experience with Python
Experience working with modern data technology (e.g. dbt, spark, containers, devops tooling, orchestration tools, git, etc.)
Experience with data science and machine learning technology