Solution Engineer – Data Engineering Specialist

🕒 May 14

🇺🇸 United States – Remote

💵 $189k - $248.1k / year

⏰ Full Time

🟠 Senior

🔴 Lead

💻 Solutions Engineer

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Snowflake

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2012

☁️ SaaS

Cloud Computing • Data Analytics • SaaS

Snowflake is a cloud-based data-warehousing company that provides a platform for data storage, processing, and analytics. It allows businesses to store data in a centralized location and perform complex queries and analytics on that data efficiently. Snowflake is designed to handle a wide range of data workloads and can scale dynamically to meet the needs of growing businesses.

📋 Description

• 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 • Immerse yourself 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

🎯 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 • 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 • Hands on Development experience with technologies such as 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 • Familiarity with streaming technologies (ex. Kafka, Flink, Spark Streaming, Kinesis) • 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

🏖️ Benefits

• Health insurance • 401(k) • Professional development opportunities