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Snowflake

Snowflake delivers the AI Data Cloud to help organizations share data, build apps and power their business with AI.

Principal Data Platform Architect – Data Engineering, Applied Field Engineering

Field EngineerField EngineerFull TimeRemoteLeadTeam 5,001-10,000Since 2012H1B SponsorCompany SiteLinkedIn

Location

Colorado

Posted

8 days ago

Salary

$189K - $248.1K / year

Seniority

Lead

Job Description

Principal Data Platform Architect – Data Engineering, Applied Field Engineering

Snowflake

• 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, 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

Job 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…)
  • 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. For example, Data Lake, Data Mesh, Data Fabric
  • Hands on Development experience with technologies such as SQL, Python, Pandas, Spark, PySpark, Hadoop, Hive and any 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 (ex. Kafka, Flink, Spark Streaming, Kinesis) and real-time or near real time use cases (ex. CDC)
  • 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, and effectively handle any impromptu questions
  • Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience *preferred*

Benefits

  • Health insurance
  • Flexible work arrangements

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