Senior Solution Engineer

About This Role

Join Snowflake's Solution Engineering team as an AI/ML specialist to provide technical guidance and support the Financial Services Industry sector. Partner with sales and solution engineering teams to understand customer AI/ML needs, strategize on sales cycles, deliver compelling demonstrations, and support Proof of Concepts.

Responsibilities

• Apply multi-cloud data architecture and AI/ML expertise while presenting Snowflake technology and vision to executives and technical contributors
• Work hands-on with prospects and customers to demonstrate and communicate the value of Snowflake technology throughout the sales cycle
• Immerse in the ever-evolving industry, maintaining a deep understanding of competitive and complementary technologies and vendors
• Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing around AI/ML features and capabilities

Requirements

• 5 - 10+ years of data engineering experience within the Enterprise Data space
• 5+ years experience working with AI/ML technologies
• Outstanding presentation skills to both technical and executive audiences
• 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 (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 experience in AI/ML tools, services and architectures in the enterprise technology space
• Clear 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

Qualifications

• Bachelor’s Degree required, computer science, engineering, mathematics or related fields, or equivalent experience preferred
• 5-10+ years of data engineering experience within the Enterprise Data space and 5+ years experience working with AI/ML technologies

Skills

* Required skills

Benefits