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Snowflake

Senior Manager, Data Engineering - Applied Field Engineering

Posted 15 Days Ago
In-Office or Remote
2 Locations
228K-299K Annually
Senior level
In-Office or Remote
2 Locations
228K-299K Annually
Senior level
Lead and manage a team of Data Engineering Specialists, engage with customers, drive revenue growth, and influence Snowflake's product strategy.
The summary above was generated by AI

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.

Our Solution Engineering organization is seeking a Senior Manager, Data Engineering - Applied Field Engineer to join our Applied Field Engineering team who will provide strategic, technical, and thought leadership to a team of specialists. This role involves personally engaging with both technical and business executives to architect the Snowflake Cloud Data Platform as a critical component of their enterprise data strategy. In this role, you will lead and manage a team working directly with the sales team to deeply understand customer needs, strategize winning sales cycles, drive revenue growth, provide compelling value-based demonstrations, support enterprise Proof of Concepts, and ultimately close business. You will leverage your expertise, best practices, and reference architectures, highlighting Snowflake’s Data Platform capabilities across data ingestion, transformation, and lakehouse workloads, all while maintaining a strong revenue and product mindset. You are equally comfortable in both a business and technical context, interacting with executives and talking shop with technical audiences.

IN THIS ROLE YOU WILL GET TO:
  • Lead, manage, mentor, and coach a team of Data Engineering Specialists to exceed individual and team revenue goals and drive high-impact customer engagements.

  • Apply your multi-cloud data architecture expertise while presenting Snowflake technology and vision to executives and technical contributors at strategic prospects, customers, and partners, serving as a key technical and thought leader.

  • Collaborate strategically with Regional Sales Leaders and Executive Account Management to target and accelerate high-value opportunities that require deep data engineering and data lake expertise

  • Work hands-on with prospects and customers as a player-coach to demonstrate and communicate the value of Snowflake technology throughout the sales cycle, from demo to proof of concept to design and implementation.

  • Contribute to the strategic direction of the AFE Data Engineering function, driving best practices, team enablement, and providing product feedback to drive future innovation.

  • 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, emphasizing the business value and ROI.

  • Influence the product roadmap, refine go-to-market strategies, and align field efforts with the product vision and customer success through close collaboration with Product Management, Engineering, Sales Leaders, and Marketing.

ON DAY ONE, WE WILL EXPECT YOU TO HAVE:
  • 10+ years of architecture and data engineering experience within the Enterprise Data space.

  • 5+ years of experience in a team management role, with a focus on pre-sales or technical field teams.

  • 5+ years experience within a pre-sales environment (Sales Engineer, Solutions Engineer, Solutions Architect, etc…).

  • Proven track record of achieving or exceeding team revenue targets.

  • Outstanding presentation skills to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos.

  • Ability to deeply connect a customer’s specific business problems with Snowflake’s solutions and articulate the associated business value and ROI.

  • Ability to do deep discovery of a 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 and business acumen 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.

Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

Top Skills

Apache Nifi
Delta
Fivetran
Flink
Hadoop
Hive
Iceberg
Informatica
Kafka
Kinesis
Matillion
Pandas
Parquet
Pyspark
Python
Qlik
Spark
Spark Streaming
SQL

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