Principal Data Platform Architect – Data Engineering, Applied Field Engineering

This is a fully remote position, open to applicants in Colorado.

📋 Description

• Leverage your expertise in multi-cloud data architecture while showcasing Snowflake's technology and vision to executives and technical contributors at key prospects, clients, and partners.

• Engage directly with prospects and clients to illustrate and convey the value of Snowflake technology throughout the sales process, from demonstrations to proofs of concept, design, and implementation.

• Immerse yourself in the dynamic industry landscape, maintaining an in-depth understanding of competitive and complementary technologies and vendors, and strategically positioning Snowflake accordingly.

• Collaborate with teams in Product Management, Engineering, and Marketing to enhance Snowflake’s products and marketing strategies.

⛳️ Requirements

• Over 10 years of experience in architecture and data engineering within the Enterprise Data domain.

• More than 5 years of experience in a pre-sales role (Sales Engineer, Solutions Engineer, Solutions Architect, etc.).

• Exceptional presentation skills for both technical and executive audiences, whether in spontaneous whiteboard sessions or through structured presentations and demos.

• Capability to link a customer's unique business challenges with Snowflake’s solutions.

• Proficiency in conducting thorough investigations of customer architecture frameworks and aligning them with Snowflake Data Architecture.

• Extensive experience with large-scale Database and/or Data Warehouse technologies, ETL, analytics, and cloud solutions, such as Data Lake, Data Mesh, and Data Fabric.

• Hands-on development experience with technologies including SQL, Python, Pandas, Spark, PySpark, Hadoop, Hive, and other Big Data technologies.

• Comprehensive understanding of data integration services and tools for constructing ETL and ELT data pipelines, such as Apache NiFi, Matillion, Fivetran, Qlik, or Informatica.

• Familiarity with streaming technologies (e.g., Kafka, Flink, Spark Streaming, Kinesis) and real-time or near real-time applications (e.g., CDC).

• Experience in designing interoperable data lakehouse architectures and working with technologies like Iceberg, Delta, and Parquet.

• Strong architectural knowledge in data engineering to present and demonstrate confidently to business executives and technical audiences, effectively addressing any impromptu inquiries.

• A Bachelor’s Degree is required; a Master’s Degree in computer science, engineering, mathematics, or related fields, or equivalent experience is preferred.

🏝️ Benefits

• Health insurance

• Flexible work arrangements