Scale Engineer

Engineering Manager, Release Validation

Engineering Manager · Manager · Full Time

US-WA-BellevueUSD 236k – 339k5d ago
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Role

What you'll do.

Lead Snowflake's Release Validation Team as an Engineering Manager responsible for building scalable test frameworks and validation platforms that enable safe, continuous deployment at enterprise scale. This role combines strategic platform ownership with hands-on technical leadership, managing a team of engineers working on CI/CD infrastructure, test automation, and developer tooling for distributed systems. You'll drive reliability and efficiency across Snowflake's release pipeline while mentoring engineers and influencing cross-functional teams to maintain the company's core brand promise of extreme reliability.

Responsibilities

  • Lead and grow the Release Validation engineering team: Hire, mentor, and develop engineers at various career stages working on test infrastructure, automation frameworks, and developer tooling. Build a culture of ownership, technical excellence, and customer-centric innovation while fostering continuous learning and career growth.
  • Define multi-quarter strategy and roadmap: Establish and drive the strategic vision for release validation platform improvements including faster test signals, reduced release blockers, improved test quality, and cost optimization. Align roadmap priorities with company objectives and communicate progress to stakeholders.
  • Own validation platform reliability and scalability: Maintain accountability for the performance, reliability, and efficiency of distributed regression testing frameworks, workload test environments, pre-production infrastructure, and end-to-end release validation pipelines. Establish SLOs and monitor platform health metrics.
  • Improve test health and quality across the organization: Partner with feature teams to identify and eliminate flaky tests, enforce test hermeticity standards, reduce test duplication, and eliminate low-value tests. Establish measurable test quality standards and create incentive structures that encourage ownership.
  • Reduce release blockers and accelerate triage: Lead initiatives to improve failure root cause analysis, filter noise from infrastructure issues, and streamline triage processes. Enable faster diagnosis and routing of genuine regressions to owning teams while reducing false positives.
  • Drive AI-powered automation and failure analysis: Invest in and implement AI-driven failure classification, intelligent routing systems, and automated analysis tools that eliminate manual engineering work. Evaluate emerging AI capabilities and rapidly prototype solutions for validation challenges.
  • Cross-functional collaboration and influence: Serve as primary partner to Release Engineering and executive leadership on release quality matters. Drive alignment across teams on testing standards, establish data-driven metrics for platform success, and influence organizational practices without direct authority.

Qualifications

What we look for.

Technical

  • CI/CD and test infrastructure expertise

    Deep experience building and operating large-scale continuous integration and continuous deployment platforms, test infrastructure systems, or developer productivity platforms. Proven ability to design systems that handle massive PR volumes and complex validation workflows.

  • Distributed systems knowledge

    Strong understanding of distributed system architecture, failure modes, and testing challenges. Experience with production simulation environments and workload testing at scale for complex, mission-critical systems.

  • Test strategy and methodology

    Comprehensive knowledge of regression testing, workload testing, test hermeticity, flakiness detection, coverage tradeoffs, and release gating strategies. Ability to make informed tradeoffs between test comprehensiveness and execution speed.

  • Data-driven systems design

    Experience designing and implementing systems that generate meaningful metrics and observability. Proficiency with dashboarding, analysis, and using data to make architectural decisions and prioritize improvements.

  • AI and automation integration

    Exposure to AI-native development practices and understanding of how machine learning can be applied to failure analysis, test optimization, and intelligent routing. Experience evaluating and prototyping emerging AI capabilities.

Education

  • Computer Science degree or equivalent

    Bachelor's degree (BS) or Master's degree (MS) in Computer Science, Computer Engineering, Software Engineering, or related technical field, or equivalent professional experience demonstrating strong foundational computer science knowledge.

Experience

  • Engineering leadership and team development

    3-5 years of direct experience hiring, coaching, and developing engineers across multiple career levels. Demonstrated ability to build high-performing teams, establish technical culture, and mentor both junior and senior engineers.

  • Platform and infrastructure development

    Proven track record building and operating large-scale platforms used internally by engineering teams. Experience managing platform evolution while maintaining backward compatibility and adoption across diverse users.

  • Cross-functional influence and communication

    Success influencing technical decisions and organizational practices across multiple teams without direct authority. Strong ability to communicate complex technical tradeoffs clearly to both technical leaders and executive stakeholders.

  • Release and quality management

    Background working with release engineering processes, quality assurance frameworks, or deployment pipelines. Understanding of how release decisions impact customer experience and company reliability metrics.

Skills

Required

  • Engineering leadership

    Proven ability to hire, develop, and mentor engineering teams with focus on growth, technical excellence, and psychological safety.

  • CI/CD pipeline architecture

    Deep technical understanding of continuous integration, continuous deployment, and release automation practices and infrastructure.

  • Test infrastructure design

    Expertise in designing scalable test frameworks, test automation platforms, and validation systems that support high-throughput execution.

  • System reliability and SRE practices

    Knowledge of reliability engineering principles, SLOs, error budgeting, and infrastructure observability for mission-critical systems.

  • Cross-team collaboration and influence

    Ability to drive alignment, build consensus, and influence technical decisions across organizations without direct reporting authority.

  • Data-driven decision making

    Proficiency with metrics definition, analysis, and using quantitative data to prioritize initiatives and demonstrate impact.

  • Technical communication

    Strong ability to explain complex technical concepts, architecture tradeoffs, and strategic decisions to diverse audiences from engineers to executives.

Preferred

  • AI and machine learning applications

    Nice to have

    Experience applying machine learning to operational challenges like failure detection, intelligent routing, or test optimization.

  • Distributed systems testing

    Nice to have

    Specific experience testing large-scale distributed systems at companies operating cloud platforms or data infrastructure at scale.

  • Developer tools and productivity

    Nice to have

    Background building developer-facing tools or platforms that improve engineering productivity and developer experience.

  • Release engineering

    Nice to have

    Direct experience working in release engineering, deployment automation, or related roles managing production release processes.

  • Flake detection and test quality

    Nice to have

    Hands-on experience identifying root causes of flaky tests, implementing hermeticity standards, and improving overall test suite quality.

  • Agentic and autonomous systems

    Nice to have

    Exposure to autonomous agent development or designing systems that operate with minimal human intervention at scale.

Tech stack

Languages

PythonJavaGoSQL

Frameworks

PytestJUnitCI/CD platformsTest automation frameworks

Databases

Snowflake Cloud Data PlatformPostgreSQL

Tools

GitHub/GitKubernetesCloud infrastructure (AWS/GCP/Azure)Observability and monitoring toolsIssue tracking and project management

Other

Distributed testing and workload generationFailure analysis and root cause analysis (RCA)Production simulation and pre-production environmentsAI-native development practices

Compensation

Pay and benefits.

Base·USD 236,000 – 339,200

Equity·Stock options

Benefits

  • Competitive health insurance

    Comprehensive medical, dental, and vision coverage with employer contributions for employee and family plans.

  • Stock options and equity

    Meaningful equity grants as part of compensation package, allowing participation in Snowflake's continued growth as a publicly traded company.

  • Retirement planning

    401(k) plan with employer matching to support long-term financial security and retirement savings.

  • Professional development

    Learning budgets, conference attendance, certification support, and access to training programs for continuous skill development.

  • Flexible work arrangements

    Flexible work location and schedule options supporting work-life balance and personal productivity preferences.

  • Generous paid time off

    Competitive vacation, sick leave, and parental leave policies enabling time for rest, recovery, and family needs.

  • Mental health and wellness

    Comprehensive mental health support, counseling services, and wellness programs promoting overall employee wellbeing.

Process

Interview steps.

  1. 01

    Initial screening call

    Brief conversation with recruiter to discuss background, interest in the role, and alignment with Snowflake's mission and culture. Typical duration: 30 minutes.

  2. 02

    Technical leadership interview

    Deep-dive conversation with hiring manager covering engineering leadership philosophy, experience building platforms, test infrastructure expertise, and approach to team development. Focus on technical depth and leadership approach. Duration: 60 minutes.

  3. 03

    Architecture and strategy discussion

    Interview with senior engineer or principal covering system design tradeoffs, experience with CI/CD platforms, test strategy decisions, and approach to solving complex infrastructure challenges. Duration: 60 minutes.

  4. 04

    Cross-functional collaboration assessment

    Conversation with Release Engineering leader or adjacent team member evaluating ability to influence without authority, communication skills, and collaborative approach to cross-team initiatives. Duration: 45 minutes.

  5. 05

    Leadership and culture conversation

    Final round with director-level or executive stakeholder assessing strategic thinking, vision alignment with Snowflake's agentic enterprise direction, and organizational impact potential. Duration: 60 minutes.

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.

About the Team

Snowflake engineering takes pride in building highly reliable and scalable products.In the era of autonomous AI agents and continuous deployment, a traditional testing model breaks down under massive PR volume. Our AI pilled engineers treat validation as a scalable, developer-first platform. The Release Validation Team builds test frameworks that make continuous deployment safe, cost-efficient, and agent-friendly. Our customers are Snowflake engineers and their army of agents. How do Snowflake engineers keep their agents running autonomous for hours and days, landing quality PRs with human interventions? They rely on layers of validation that agents can execute in high-fidelity production simulation.

We give Snowflake the confidence to ship at speed without compromising Snowflake’s core brand promise: extreme reliability.

In this role, you will:

  • Lead and grow the team. Hire, mentor, and develop engineers working on test infrastructure, automation, and developer tooling. Build a culture of ownership, technical excellence, and focus on the customer.

  • Set strategy and roadmap. Define and drive the multi-quarter vision for release validation: faster signals, fewer blockers, better test quality, and lower cost.

  • Own the validation platform. Be accountable for the reliability, scalability, and efficiency of our distributed regression and workload test frameworks, pre-production environments, and release validation pipeline..

  • Improve test health across the company. Work with feature teams to reduce flaky, non-hermetic, duplicate, and low-value tests, and set standards and incentives that keep them healthy.

  • Cut down release blockers. Lead efforts to improve how failures are traced to their cause, filter out noise from infrastructure problems, and speed up triage so real regressions reach the owning team quickly.

  • Drive AI and automation. Invest in AI-driven failure analysis and automated routing that remove manual work for engineers across Snowflake.

  • Work across teams. Act as the main partner to Release Engineering and engineering leaders on release quality

What We're Looking For

  • 3-5 years of experience hiring, coaching, and developing engineers at different career stages.

  • Experience building and running large-scale CI/CD, test infrastructure, or developer productivity platforms, ideally for complex distributed systems.

  • A strong technical background, with enough depth to review designs, make architecture tradeoffs, and earn the respect of senior engineers.

  • A solid grasp of test strategy: regression vs. workload testing, hermeticity, flakiness, coverage tradeoffs, and release gating.

  • Experience driving work across many teams and influencing without authority.

  • A data-driven leadership style: you set measurable goals and use metrics to prioritize and show impact.

  • Strong communication skills, including explaining complex technical tradeoffs to both engineers and executives.

  • BS/MS in Computer Science or related majors, or equivalent experience.

 

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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