Principal Data Platform Architect

Provides pre-sales technical leadership for enterprise data platform architecture, demonstrations, proof-of-concepts, and implementations. The role requires 10+ years of data architecture experience, 5+ years in pre-sales, strong executive communication, and hands-on expertise across cloud, lakehouse, integration, and streaming technologies.

About the job

Responsibilities

Provide technical leadership to technical and business executives in designing and architecting the Snowflake Cloud Data Platform within enterprise data architectures and ecosystems.
Partner with sales teams to understand customer needs, navigate sales cycles, deliver value-based demonstrations, support enterprise proof-of-concepts, and close business.
Present Snowflake technology and vision to executives and technical contributors at strategic prospects, customers, and partners.
Demonstrate and communicate Snowflake's value throughout the sales cycle, from demos and proof-of-concepts through design and implementation.
Apply multi-cloud data architecture expertise across data ingestion, transformation, and lakehouse workloads.
Evaluate competitive and complementary technologies and vendors and position Snowflake accordingly.
Collaborate with Product Management, Engineering, and Marketing to improve products and marketing.

Requirements

10+ years of architecture and data engineering experience in the enterprise data space.
5+ years of pre-sales experience, such as Sales Engineer, Solutions Engineer, or Solutions Architect.
Outstanding presentation skills for technical and executive audiences, including impromptu whiteboarding, presentations, and demos.
Ability to connect customer business problems with Snowflake solutions and conduct deep discovery of customer architecture frameworks.
Broad experience with large-scale databases, data warehouses, ETL, analytics, and cloud technologies, including data lakes, data mesh, and 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 ETL and ELT pipelines, including Apache NiFi, Matillion, Fivetran, Qlik, or Informatica.
Familiarity with streaming technologies and real-time or near-real-time use cases, including CDC.
Experience designing interoperable data lakehouse architectures and working with Iceberg, Delta, and Parquet.
Strong data engineering architecture expertise for presentations and demonstrations to business and technical audiences.
Bachelor's degree required. A master's degree in computer science, engineering, mathematics, or a related field, or equivalent experience, is preferred.