Sr. Manager Performance and Cost, Applied Field Engineering
SnowflakeAbout the role
Where Data Does More. Join the Snowflake team.
As Senior Manager, Performance & Cost (Applied Field Engineering), you will lead a team of specialized engineers focused on solving Snowflake’s most complex customer challenges in performance, scale, and cost efficiency. This is a high-impact leadership role where you’ll build and grow a world-class team that serves as a core technical resource for our most strategic enterprise clients. The role requires a passion for developing people, a hands-on understanding of data and AI performance, and the ability to deliver cloud economics at scale.
Snowflake’s Performance & Cost team helps customers run faster, cheaper, and safer on the AI Data Cloud. We address the hardest field problems—query performance, workload design, FinOps, data modeling, and extensibility (UDFs/UDTFs/Procedures)—and turn them into repeatable practices, assets, and showcases for the field and our customers. In this role, you will partner closely with Sales, Product, and Support, act as the Voice of the Field to Engineering, and set the standard for industry-grade performance engineering and cost optimization, aligned with well-architected principles across the market.
WHAT YOU'LL GET TO DO
Lead & Scale a Specialist Practice
Build, coach, and grow a high-performing team of senior performance engineers and architects.
Set operational rhythms and KPIs to measure runtime improvements, cost savings, and asset adoption.
Drive Customer Outcomes (Performance + Cost)
Understand concepts on diagnosing complex workloads (query plans, clustering, concurrency, warehouse sizing) and deliver measurable performance/cost improvements.
Establish FinOps guardrails and workload patterns that optimize efficiency and align to customer SLAs
Codify Best Practices & DML Patterns
Create prescriptive guidance for DML, table design, semi-structured data, and workload orchestration.
Turn advanced patterns into repeatable assets that scale across customers and the field.
UDF/UDTF/Procedure Excellence
Define best practices for SQL/JavaScript UDFs, UDTFs, and procedures to maximize performance and extensibility.
Govern safe, scalable usage and publish guidance for field and customer adoption.
Hands-on Solution Showcases
Build and scale demos, reference apps, videos, and AI agentic experiences that make performance and cost outcomes tangible.
Package showcases for field reuse and customer workshops to accelerate adoption.
Field Enablement & Thought Leadership
Lead performance and cost workshops, bootcamps, and design-thinking sessions with customers and partners.
Publish reference architectures, playbooks, and checklists that embed best practices in the field.
Influence Product & Roadmap
Represent the Voice of the Field to Product and Engineering, prioritizing features that impact performance and cost.
Validate new capabilities with benchmarks, canary testing, and real-world case studies.
WHAT WE'LL EXPECT ON DAY ONE
Experience: 8+ years in data platforms/performance engineering or field engineering; 3+ years directly leading senior ICs (hiring, coaching, performance).
Technical Expertise: Proven expertise in data and AI platforms, cloud computing, and cloud security. This includes a deep understanding of cloud security frameworks, performance optimization, and cloud cost management.
Deep expertise in: SQL/DML optimization, workload orchestration, concurrency management, warehouse sizing/queues, table design & clustering, semi-structured data (JSON/VARIANT), materialized views, and query plan analysis.
Proven cloud cost optimization track record (rightsizing, scaling policies, storage/compute tradeoffs, scheduler hygiene, governance and alerts) aligned with well-architected cost pillars.Hands-on with UDF/UDTF/Stored Procedure design and performance considerations (JavaScript & SQL), including predicate pushdown strategies and serialization/mutex patterns.
Ability to turn hard problems into repeatable assets: runbooks, checklists, scripts, notebooks, and field-
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