Solution Engineer - Data Engineering Specialist (FSI)
Provides technical leadership in designing Snowflake Cloud Data Platform architectures for Financial Services clients, supporting sales with demos, POCs, and value demonstrations. Requires 10+ years data engineering experience and 5+ years pre-sales expertise.
189k – 248k
On-site10+ YOESales Engineering
About the role
Responsibilities
Apply 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 (e.g., Data Lake, Data Mesh, Data Fabric)
Hands-on development experience with SQL, Python, Pandas, Spark, PySpark, Hadoop, Hive and 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 (Kafka, Flink, Spark Streaming, Kinesis) and real-time or near real-time use cases (e.g., CDC)
Experience designing interoperable data lakehouse architectures and working with Iceberg, Delta, and Parquet
Strong architectural expertise in data engineering to confidently present and demo to business executives and technical audiences
Bachelor’s Degree required; Master’s Degree in computer science, engineering, mathematics or related fields preferred
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