Data Platform Architect (Data Engineering (Streaming Solutions))

Role Overview

As a Data Platform Architect (Data Engineering / Streaming Solutions) at Snowflake, you provide technical leadership for the design and architecture of the Snowflake Cloud Data Platform. You partner with the sales team and work hands-on with prospects and customers to demonstrate value and drive enterprise Proof of Concepts.

Key Missions

• Collaborate with Sales to understand customer needs and provide technical leadership for Snowflake’s cloud data platform architecture.
• Leverage data engineering and multi-cloud architecture expertise to communicate Snowflake value throughout the sales cycle.
• Support enterprise Proof of Concepts (POCs) with hands-on work for prospects and customers.
• Work with Product, Engineering, and Marketing to continuously improve Snowflake offerings and messaging.

Responsibilities (What You’ll Do)

• Design and present data engineering architectures aligned to customer requirements.
• Conduct value-based demonstrations and respond to technical questions from both executive and technical audiences.
• Perform deep discovery of customer architecture frameworks and map those needs to Snowflake data architecture.
• Help evaluate/shape customer ETL/ELT and streaming requirements for real-time or near real-time use cases.

Requirements

• 10+ years of architecture and data engineering experience in the enterprise data space.
• Broad experience with large-scale database/data warehouse, ETL/analytics, and cloud technologies (e.g., Data Lake, Data Mesh, Data Fabric).
• Strong presentation and demo skills for both technical and executive audiences.
• Demonstrable architectural expertise in data engineering for enterprise demonstrations and Q&A.
• Familiarity with streaming technologies such as Kafka, Flink, Spark Streaming, or Kinesis, plus real-time/near real-time use cases (e.g., CDC).
• 5+ years pre-sales experience (e.g., Sales Engineer, Solutions Engineer, Solutions Architect).
• Hands-on development experience with SQL, Python, Pandas, Spark/PySpark, Hadoop, Hive, and other big data technologies.
• Experience designing interoperable data lakehouse architectures, including Iceberg, Delta, and Parquet.
• Ability to connect customer business problems to Snowflake solutions.
• Deep understanding of data integration services and ETL/ELT pipelines, including tools such as Apache NiFi, Matillion, Fivetran, Qlik, or Informatica.
• Bachelor’s degree required (Master’s preferred) in CS/engineering/math or equivalent experience.

Nice-to-Haves

• Master’s degree in a relevant field.
• Additional experience across multi-cloud streaming and CDC-driven architectures.