Staff QA (Infra Testing, Performance)

Responsibilities

Performance Engineering & Load Modelling

• Design realistic workload models based on production usage patterns

• Define throughput, concurrency, and scalability benchmarks

• Conduct capacity planning and performance benchmarking

Test Environment Engineering & Automation

• Build and manage production-like test environments

• Automate environment provisioning using Infrastructure-as-Code (IaC)

• Enable self-service, on-demand environments for teams

Test Data Management (TDM) & Engineering

• Define and implement enterprise-grade Test Data Management strategy

• Manage test data lifecycle including creation, refresh, masking, and archival

• Ensure high-quality, compliant, and production-representative datasets

• Enable data versioning and reusability across test cycles

AI-Driven Test Data Creation & Synthetic Data Engineering

• Leverage AI/GenAI tools to generate intelligent, scenario-based test data

• Design and implement synthetic data generation frameworks for:

-Edge cases and negative scenarios

-Large-scale performance datasets

-Privacy-safe testing

• Ensure synthetic data maintains statistical similarity to production data

Data Replication, Masking & Environment Data Strategy

• Implement secure data replication pipelines from Production → UAT/Test environments

• Apply data masking, anonymization, and tokenization techniques to ensure compliance

• Optimize data refresh cycles for performance and cost efficiency

• Collaborate with security and compliance teams to enforce data governance policies

Performance Testing & Execution

• Design and execute load, stress, endurance, and spike tests

• Use tools like JMeter, Gatling, LoadRunner for distributed load simulation

• Analyze system behavior and identify performance bottlenecks

Deep System Troubleshooting

• Diagnose issues across:

-Application (APIs/services)

-Database (query optimization, indexing)

-Infrastructure (CPU, memory, network latency)

• Perform root cause analysis (RCA) and drive resolution

Observability & Benchmarking

• Define and implement monitoring and observability frameworks

• Establish performance baselines, dashboards, and reporting

• Provide actionable insights into system health and scalability

Cross-Team Collaboration & Enablement

• Drive shift-left performance and data strategies

• Partner with engineering teams to ensure testability and scalability

• Mentor teams on performance engineering, TDM, and automation best practices

AI-Driven Quality Engineering

• Leverage AI/ML tools to accelerate test creation, optimization, and analysis

• Automate anomaly detection and performance insights

Nice to Have

• Experience with cloud platforms (AWS, Azure, GCP)

• Knowledge of containerization (Docker, Kubernetes)

• Experience with observability tools (Prometheus, Grafana, ELK, AppDynamics, Dynatrace)

• Exposure to chaos engineering and resilience testing

Leadership

• Dives deep into the root cause of  issues and ensures that the issues remain fixed

• Collaborates with and influences team members; mentors junior engineers

• Views problems as opportunities and proposes solutions

• Acts like an owner; thinks on behalf of customers

• Able to navigate  challenges faced in achieving goals or reach out for help in a timely manner

• Mentors junior engineers

What Success Looks Like

• Production-like environments with high-fidelity test data

• Scalable systems validated through realistic load and data models

• Reduced defects through early performance and data validation

• Strong adoption of TDM, synthetic data, and automation practices