Solution Engineer - Data Engineering Specialist (FSI)

Snowflake is empowering enterprises to achieve their full potential by fostering a culture of impact, innovation, and collaboration. The Data Engineering Specialist will provide technical leadership in the design and architecture of the Snowflake Cloud Data Platform, working closely with sales teams and customers to navigate sales cycles and demonstrate the value of Snowflake's technology.

Responsibilities:

• Apply your multi-cloud data architecture expertise while presenting Snowflake technology and vision to executives and technical contributors at strategic prospects, customers, and partners
• 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
• Immerse 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
• Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing

Requirements:

• 10+ years of architecture and data engineering experience within the Enterprise Data space
• 5+ years experience within a pre-sales environment (Sales Engineer, Solutions Engineer, Solutions Architect, etc…)
• Proven experience working with Financial Services customers
• Outstanding presentation skills to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos
• Ability to connect a customer's specific business problems and Snowflake's solutions
• Ability to do deep discovery of customer's architecture framework and connect those with Snowflake Data Architecture
• Broad range of experience within large-scale Database and/or Data Warehouse technology, ETL, analytics and cloud technologies. For example, Data Lake, Data Mesh, Data Fabric
• Hands on Development experience with technologies such as SQL, Python, Pandas, Spark, PySpark, Hadoop, Hive and any other Big data technologies
• Deep understanding of data integration services and tools for building ETL and ELT data pipelines such as Apache NiFi, Matillion, Fivetran, Qlik, or Informatica
• Familiarity with streaming technologies (ex. Kafka, Flink, Spark Streaming, Kinesis) and real-time or near real time use cases (ex. CDC)
• Experience designing interoperable data lakehouse architectures and experience working with Iceberg, Delta, and Parquet
• Strong architectural expertise in data engineering to confidently present and demo to business executives and technical audiences, and effectively handle any impromptu questions
• Bachelor's Degree required
• Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred