Quality Engineer

Experience Required

• 5+ years of experience in Performance Testing and Performance Engineering.
• Proven experience leading performance validation efforts, capacity planning, and mentoring engineering teams.

Essential functions

Key Responsibilities

• Lead automation efforts to accelerate MineStar releases while maintaining high standards of quality and safety for mining operations.
• Define and implement MineStar-specific test strategies, metrics, and KPIs to assess product quality and release readiness.
• Build and maintain automation frameworks covering MineStar UI, APIs, services, and data flows.
• Integrate automation into CI/CD pipelines to enable continuous validation during MineStar modernization and re-platforming.
• Collaborate closely with Test Leads, Product Managers, System Architects, and Platform teams to align quality with MineStar's target architecture.
• Mentor QA engineers (manual and automation) and support their transition to automation-first, quality-engineered practices.

Qualifications

Key Responsibilities

• Strategy & Leadership: Define and execute the end-to-end performance testing strategy, workload models, and NFR (Non-Functional Requirements) benchmarks for the MineStar platform.
• Testing at Scale: Design, script, and execute load, stress, endurance, spike, and scalability tests simulating high-concurrency, real-world site operations.
• Architecture Modernization: Validate system behavior, throughput, and latency during MineStar’s ongoing re-platforming and cloud/microservices transition.
• Continuous Performance (CI/CD): Integrate automated performance tests into CI/CD pipelines to catch performance regressions early in the development lifecycle.
• Root Cause Analysis & Optimization: Partner with Architecture, Platform, and DB teams to analyze bottlenecks (CPU, memory leaks, thread locks, network latency, slow DB queries) and recommend actionable fixes.
• Mentorship & Process: Mentor QA and development engineers on performance testing best practices, baseline monitoring, and performance-first engineering mindsets.

Skills Descriptor

• Performance Tooling: Hands-on expertise with industry-standard performance testing tools (e.g., JMeter, Locust, Gatling, or Neoload).
• System Architecture: Proven experience testing microservices-based architectures, REST/gRPC APIs, event-driven systems (e.g., Kafka, RabbitMQ), and on-premise/hybrid cloud deployments.
• Monitoring & APM: Deep experience with Application Performance Monitoring (APM) tools (e.g., Dynatrace, AppDynamics, Datadog, New Relic) and log analytics (Splunk, ELK stack).
• Database & Protocol Validation: Strong ability to analyze database performance, write complex SQL queries, and diagnose slow-running queries and indexing issues.
• CI/CD Integration: Experience integrating performance scripts into modern build pipelines (e.g., Azure DevOps, Jenkins, GitLab CI).
• Agile & Communication: Excellent communication and analytical skills, with a track record of collaborating with cross-functional, global teams to report performance metrics effectively.

AI-Enabled Performance Engineering

• Leverage AI-assisted tools to optimize load distribution, accelerate workload model creation, analyze telemetry data, and automate root-cause detection for performance bottlenecks.
• Promote responsible, secure, and governed adoption of AI tools across performance engineering practices.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.