Quality System Engineer

Omnissa•Atlanta, GA

4d•$118,800 - $160,000•Hybrid

About The Position

We are seeking a Quality Systems Engineer to own product quality end-to-end from test strategy and automation to the full lifecycle of field-reported issues that reach engineering. This is a core engineering role, not a support function. It sits at the intersection of proactive quality ownership and reactive field intelligence, treating every real-world issue as a signal that strengthens the system. Quality Systems Engineers design and build the test gates that keeps the product reliable. They also serve as the engineering first responder when complex, high-priority issues arrive from the field — investigating, reproducing, and distilling them into precise, engineering-ready work items. Critically, the patterns they uncover flow back into test coverage, observability improvements, and product design, closing the loop between what customers experience and what engineering builds next. This role owns metrics across two dimensions: the health of the product as measured through traditional quality indicators, and the health of the field feedback pipeline as measured through incoming service request volume, field issue triage velocity, and the rate at which systemic issues are eliminated. Both dimensions matter equally.

Requirements

Strong software engineering foundation with the ability to read, debug, and reason across distributed systems and microservices
Hands-on experience in software quality engineering, including test design, test automation, and defect lifecycle management
Experience investigating production systems using logs, monitoring tools, and observability platforms
Solid understanding of APIs, client-server architectures, and modern distributed systems
Strong analytical and structured problem-solving ability for ambiguous, high-priority issues
Excellent written and verbal communication skills for cross-functional work with Engineering, TPMs, and product stakeholders

Nice To Haves

Experience in enterprise device management (UEM) domains such as Apple, Android, Windows, or similar platforms
Familiarity with AI-assisted engineering tools (Cursor, Claude, Rovo, or equivalent)
Background in sustaining engineering, field engineering, or production systems engineering
Experience building or owning test automation frameworks or developer productivity tooling
Familiarity with observability platforms and structured log analysis

Responsibilities

Design, develop, and maintain test plans, test cases, and automated test suites covering functional, regression, integration, and system-level scenarios
Own test automation strategy and contribute to frameworks that improve coverage, speed, and reliability across release cycles
Evaluate the product through the lens of usability and supportability — not just correctness — and proactively surface gaps before they reach customers
Participate in design and code reviews to embed quality thinking upstream in the development lifecycle
Track and report on quality health metrics including defect escape rates, test coverage, automation pass rates, and release readiness signals
Contribute to improvements in logging, diagnostics, and system observability that make the product easier to validate and debug
Act as the engineering first responder for complex, high-priority issues arriving from the field, owning investigation and triage end-to-end
Perform rapid technical analysis using logs, monitoring systems (e.g., Logz.io: Modern Observability Powered by AI ), and production data to assess severity, impact scope, and failure patterns
Conduct deep technical debugging across distributed systems, APIs, services, and code paths to isolate and narrow suspected root causes
Reproduce complex field issues in local or shared environments, ensuring reliable alignment with production behavior
Enrich issue tickets with complete technical context — logs, configurations, network traces, reproduction steps, and system behavior details — so engineering teams can act without additional clarification
Identify duplicate issues and link them to existing defects or known issues to maintain clean, accurate JIRA hygiene
Collaborate with Core Engineering to validate hypotheses and confirm failure points through structured analysis
Validate fixes in patches or releases and ensure proper issue closure including regression verification
Analyze patterns across field issues and incoming customer service requests to identify systemic gaps in test coverage, observability, or product behavior
Translate field intelligence into concrete engineering improvements: new test cases, observability instrumentation, documentation, or product design changes
Participate in retrospectives for significant incidents and ensure findings produce durable improvements — not just one-time fixes
Partner with TPMs and Engineering leadership to track field issue SLAs and resolution progress across priority tiers
Build and contribute to automation, scripts, and AI-assisted workflows that reduce repetitive triage work and improve engineering throughput
Set up and maintain development and test environments required for issue reproduction and validation workflows
Recommend and implement improvements to system observability, UX signals, and diagnostics that reduce future field issue volume