Scale Engineer

Principal Software Engineer - Performance Engineering (Cloud Infrastructure)

Principal Engineer · Principal · Full Time

US-CA-Menlo ParkUSD 264k – 380k1w ago
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Role

What you'll do.

Principal Software Engineer leading cloud infrastructure performance strategy at Snowflake, responsible for hardware evaluation across AWS, Azure, and GCP, building benchmarking infrastructure, and driving price/performance decisions. This high-autonomy role combines deep systems knowledge with cross-functional technical leadership, requiring expertise in CPU microarchitecture, cloud infrastructure performance, and large-scale performance engineering to shape the future of Snowflake's cloud platform capabilities.

Responsibilities

  • Lead Hardware Evaluation and Enablement: Own new hardware evaluation across all major cloud providers including AWS (Graviton, Trainium, Inferentia), Azure (ARM/Cobalt), and GCP (Axion). Build representative benchmark and evaluation solutions to assess new instance types, analyze performance characteristics, and develop technical recommendations for production rollout and adoption strategies.
  • Shape Performance Engineering Strategy and Architecture: Set the technical roadmap for the Capacity, Performance & Efficiency team, establish measurement methodology standards, and translate raw benchmark results into concrete adoption recommendations. Define performance objectives aligned with business economics and cloud provider partnerships while championing data-driven decision making across infrastructure initiatives.
  • Drive Cross-Functional and CSP Partnership Programs: Serve as primary technical point of contact with AWS, Azure, GCP, and silicon vendors. Lead partnership initiatives to qualify new hardware, influence future cloud instance designs, and align Snowflake's platform strategy with cloud provider technical roadmaps. Coordinate with Warehouse, Capacity, and platform teams alongside Finance to drive enterprise-wide adoption strategies.
  • Characterize Hardware Performance at Architectural Depth: Conduct deep-level analysis of CPU microarchitecture, memory bandwidth utilization, I/O behavior patterns, and performance monitoring unit (PMU) counter data. Identify workload-specific bottlenecks through profiling and systems analysis, root-cause performance regressions, and drive targeted resolution across hardware generations and cloud provider offerings.
  • Develop Price-to-Performance Models: Build quantitative models connecting technical performance metrics to business outcomes including pricing multipliers and cost-per-query economics. Transform benchmark data into pricing recommendations and capacity planning decisions that optimize Snowflake's competitive positioning and unit economics across diverse cloud infrastructure options.
  • Automate Hardware Readiness and Validation: Design and implement automated performance validation workflows, qualification pipelines, and per-provider/per-instance scorecards. Enable Snowflake to rapidly qualify and price new silicon at or near cloud provider general availability through systematic automation, reducing manual analysis and accelerating time-to-production decisions.
  • Build Performance Engineering Discipline and Culture: Mentor performance engineers and systems engineers within the organization, establish benchmarking best practices, and champion company-wide performance education through recurring tech talks and knowledge-sharing initiatives. Develop the next generation of performance engineering expertise while scaling the discipline across Snowflake's engineering organization.

Qualifications

What we look for.

Technical

  • CPU Microarchitecture Analysis

    Expert-level understanding of modern CPU microarchitecture including cache hierarchies, branch prediction, instruction-level parallelism, memory bandwidth constraints, and performance monitoring unit (PMU) counters. Ability to interpret low-level performance metrics to identify workload-specific bottlenecks and correlate hardware characteristics to application behavior.

  • Cloud Infrastructure Performance Engineering

    Deep expertise in cloud infrastructure performance across at least one major CSP (AWS, Azure, or GCP). Proficiency with instance type selection, pricing models, capacity planning, resource constraints, and performance characteristics specific to cloud environments. Experience optimizing workloads for cloud-native architectures and managing multi-cloud deployment strategies.

  • Large-Scale Benchmarking Systems

    Demonstrated experience building, deploying, and operating large-scale benchmarking infrastructure. Proficiency with industry-standard benchmarks (TPC-DS, TPC-H, custom workload models) and ability to design representative test scenarios that accurately reflect production workloads and performance patterns.

  • Performance Profiling and Analysis Tools

    Expert proficiency with Linux profiling tools, kernel tracing utilities (perf, ftrace, bpf), hardware performance counters, and systems analysis frameworks. Ability to collect, analyze, and interpret low-level performance data to drive root-cause analysis and optimization recommendations.

  • Quantitative Performance Modeling

    Strong ability to develop mathematical and statistical models that translate raw technical metrics into business outcomes. Experience building pricing models, cost optimization frameworks, and predictive models that connect infrastructure performance to revenue, margins, and strategic business decisions.

  • Systems Engineering and Infrastructure Architecture

    Comprehensive understanding of distributed systems, infrastructure architecture, and systems-level optimization. Knowledge of storage systems, networking, I/O subsystems, and the interactions between hardware and software that impact end-to-end system performance.

  • Automation and Infrastructure-as-Code

    Proficiency building automated workflows, validation pipelines, and scorecard systems. Experience with infrastructure automation tools and ability to translate manual processes into scalable, repeatable automation that improves operational efficiency and decision velocity.

Education

  • Bachelor's Degree in Computer Science or Related Field

    Fundamental understanding of computer systems, algorithms, and software engineering principles. Educational foundation in systems architecture, performance analysis, and computational theory that supports advanced infrastructure engineering work.

Experience

  • 12+ Years in Performance or Systems Engineering

    Extensive track record in performance engineering, systems engineering, or infrastructure engineering roles demonstrating progressive technical leadership. Proven ability to deliver high-impact projects, drive technical strategy, and influence organizational decisions at scale.

  • Principal-Level Technical Leadership

    Demonstrated experience in senior technical leadership roles where you have defined strategies, influenced cross-functional initiatives, and mentored other engineers. Track record of making independent decisions on complex technical matters and representing the organization with external partners.

  • Cross-Functional and External Partnership Leadership

    Proven ability to lead initiatives requiring coordination across engineering teams, business functions, and external organizations. Experience serving as primary technical point of contact with hardware vendors, cloud providers, or other strategic partners. Demonstrated success in negotiating technical requirements and aligning diverse stakeholder interests.

  • Data Warehouse or Distributed Database Experience (Preferred)

    Direct experience optimizing performance for data warehouse platforms, distributed databases, or large-scale analytical systems. Understanding of specific performance characteristics, optimization strategies, and architectural considerations unique to data-intensive applications.

  • Cloud Provider or Silicon Vendor Partnership (Preferred)

    Direct experience participating in early-access hardware programs, partnering with cloud providers on platform optimization, or collaborating with silicon vendors on custom silicon initiatives. Experience navigating vendor relationships and translating emerging capabilities into business value.

Skills

Required

  • Hardware Performance Analysis

    Deep expertise analyzing CPU and hardware performance characteristics using low-level profiling, PMU data interpretation, and microarchitecture analysis to identify bottlenecks and drive optimization strategies.

  • Cloud Infrastructure Expertise

    Expert-level knowledge of AWS, Azure, or GCP infrastructure including instance types, pricing models, capacity management, and performance characteristics across major cloud providers.

  • Performance Benchmarking

    Demonstrated ability to design, build, and operate large-scale benchmarking systems including workload modeling, benchmark implementation, and translating results into actionable business insights.

  • Systems and Infrastructure Engineering

    Comprehensive understanding of distributed systems architecture, I/O subsystems, memory hierarchies, and the complete stack from hardware through software that impacts overall system performance.

  • Quantitative Modeling and Business Analysis

    Proficiency developing models that connect technical performance metrics to business outcomes including pricing, cost optimization, and revenue impact. Ability to translate technical results into business recommendations.

  • Technical Leadership and Communication

    Strong ability to lead technical initiatives, mentor engineers, communicate complex technical concepts to diverse audiences, and represent the organization as primary technical contact with external partners.

  • Linux Systems and Kernel Understanding

    Deep knowledge of Linux kernel, system-level APIs, process scheduling, memory management, and I/O handling that supports low-level performance analysis and optimization work.

  • Data-Driven Decision Making

    Demonstrated commitment to rigorous measurement, empirical analysis, and evidence-based recommendations. Ability to build dashboards, scorecards, and automated systems that support continuous performance optimization.

Preferred

  • Data Warehouse Performance

    Nice to have

    Experience optimizing data warehouse or distributed database workloads with understanding of analytical query patterns, columnar storage characteristics, and specific performance optimization challenges in data-intensive systems.

  • Silicon Design or Hardware Emulation

    Nice to have

    Experience with hardware emulation systems, custom silicon design partnerships, or architectural work with silicon vendors. Understanding of early-stage hardware development and the path from prototype to production deployment.

  • Advanced Profiling Tools

    Nice to have

    Proficiency with advanced Linux profiling tools, kernel-level tracing (perf, ftrace, eBPF), and hardware performance counter interpretation for extracting maximum insight from low-level system data.

  • Programming Languages for Systems Work

    Nice to have

    Proficiency with C, C++, Python, or Rust for building performance analysis tools, benchmarks, and automation infrastructure. Ability to write efficient systems-level code that accurately measures and demonstrates performance characteristics.

  • ML/AI Infrastructure Performance

    Nice to have

    Experience optimizing machine learning or AI workloads on cloud infrastructure including GPU/accelerator performance, distributed training optimization, and inference performance tuning.

  • Vendor Relationship Management

    Nice to have

    Experience managing technical relationships with hardware vendors, cloud providers, or strategic partners. Demonstrated ability to influence partner roadmaps and negotiate technical requirements.

Tech stack

Languages

PythonC/C++SQLBash

Frameworks

TPC-DS/TPC-H BenchmarksMLPerfApache Arrow

Databases

SnowflakeCloud Data Warehouses

Tools

perf/Linux ProfilersAWS/Azure/GCP Management ConsolesTerraformGrafana/PrometheusGit/Version ControlJIRA/Project Management

Other

CPU Microarchitecture AnalysisCloud Instance Type OptimizationWorkload CharacterizationPrice-to-Performance ModelingDistributed Systems Performance

Compensation

Pay and benefits.

Base·USD 264,000 – 379,500

Equity·Stock options

Full posting

Original listing.

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

Principal Software Engineer — Performance Engineering (Cloud Infrastructure)

Engineering Location: Menlo Park

About the Team

The Capacity, Performance & Efficiency team sits at the intersection of cloud hardware, workload behavior, performance measurement, and business economics. We drive Snowflake’s cloud hardware adoption and evolution strategy, price/performance decisions, platform footprint expansion amidst capacity constraints, and strategic alignment with cloud service provider (CSP) technical roadmaps.

We're looking for a Principal Performance Engineer to drive Snowflake's cloud infrastructure performance strategy. This is a high-autonomy, high-impact role for someone who can go deep on hardware internals (CPU microarchitecture, memory bandwidth, I/O behavior) while also translating that technical depth into pricing, capacity, and roadmap decisions across Snowflake.

You will drive the evaluation of new hardware generations across all major cloud providers, build the benchmarking and modeling infrastructure that turns raw performance data into pricing and rollout decisions, and represent Snowflake in technical discussions with CSPs and silicon partners. You'll help grow performance engineering as a discipline across the company — mentoring engineers, building benchmark capability, and championing a culture of rigorous, data-driven performance work.

What You'll Do

  • Own new hardware evaluation and enablement. Lead evaluation of new AWS, Azure, and GCP instance types (e.g., Graviton, AMD Turin, Azure ARM/Cobalt, GCP Axion) by building representative benchmark and evaluation solutions.

  • Set performance strategy and architecture. Shape the team's technical roadmap, set direction on measurement methodology, and translate benchmark results into concrete rollout and adoption recommendations.

  • Lead cross-functional and CSP partnerships. Work across engineering (i.e. Warehouse, Capacity, and other core platform teams) and Finance along with AWS, Azure, GCP to qualify new hardware and influence future cloud-instance designs.

  • Characterize hardware performance at a deep level. Analyze CPU microarchitecture (e.g., memory bandwidth, PMU counters, I/O behavior) to identify workload-specific bottlenecks and drive resolution.

  • Develop price/performance model to support new hardware transitions.

  • Automate day-zero hardware readiness. Build automated performance-validation workflows and per-provider/per-instance scorecards so Snowflake can qualify and price new silicon at or near cloud-provider GA.

  • Grow the performance engineering discipline. Mentor engineers, build out benchmarking capability, and drive company-wide performance education (e.g., a recurring Performance Tech Talk series).

Our ideal candidate will have:

  • 12+ years of experience in performance engineering, systems engineering, or infrastructure engineering, with a track record of principal-level technical leadership.

  • Deep expertise in cloud infrastructure performance across at least one major CSP (AWS, Azure, or GCP), including instance types, pricing models, and capacity constraints.

  • Strong grounding in hardware/systems fundamentals: CPU microarchitecture, memory bandwidth, I/O subsystems, and use of profiling tools (PMU counters, etc.) to root-cause performance bottlenecks.

  • Experience building or operating large-scale benchmarking systems (e.g., TPC-DS-style workloads) and turning benchmark output into actionable pricing/rollout decisions.

  • Ability to build quantitative models connecting technical performance metrics to business outcomes (pricing multipliers, cost-per-query).

  • A track record of leading cross-functional, cross-company initiatives — comfortable being the primary technical point of contact with external hardware and cloud partners (CSPs, silicon vendors).

  • Bias toward automation: builds scorecards, dashboards, and validation pipelines rather than relying on one-off analysis.

Nice to Have

  • Experience with data warehouse / distributed database performance characteristics.

  • Direct experience partnering with cloud providers or silicon vendors on early-access hardware programs.

  • Experience building hardware-emulation systems.


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

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