Senior Performance QA Engineer @ Infobeans

• Design and implement end-to-end performance testing strategies for banking/financial applications, including API, query-layer (OData), and data pipeline performance

• Execute load, stress, endurance, and volume tests for APIs, microservices, and data-intensive workloads (large datasets, aggregation, and export scenarios).

• Develop and maintain performance test scripts using tools including Python-based frameworks like Locust

• Analyze system performance and identify bottlenecks across application, database, and infrastructure layers

• Validate pagination correctness, data consistency, and query behavior under concurrent load 

• Collaborate with development, DevOps, and business teams to ensure performance optimization

• Integrate performance tests into CI/CD pipelines and enable continuous performance testing

• Work with large datasets and validate performance of data processing pipelines (including Azure Databricks workloads)

• Generate interactive and user-friendly performance reports (HTML/UI-based) for stakeholders

• Monitor application and infrastructure using APM tools and logs, and correlate frontend-driven traffic patterns with backend performance

• Perform capacity planning and provide recommendations for scaling systems based on measured throughput, concurrency, and resource utilization

• Strong experience with Python-based performance testing tools only:

• Locust (mandatory)

• Hands-on experience with:

• Azure Databricks (large-scale data processing and performance considerations)

• Azure DevOps

• Jira

• GitHub Actions

• Reporting & Visualization:

• Experience generating HTML-based performance reports (custom Python reports or frameworks)

• Hands-on with Allure Report for rich UI reporting

• Experience with dashboarding tools like Grafana for real-time insights

• Monitoring tools:

• Dynatrace

• AppDynamics

• Prometheus + Grafana

• Strong understanding of:

• APIs (REST/GraphQL), Web Services, and distributed systems

• Microservices and event-driven architectures

• Query-driven systems (filtering, pagination, aggregation, OData-like patterns)

• Performance characteristics of data-intensive systems (large datasets, caching, IO vs CPU bound workloads)

• Proficiency in Python (mandatory) for scripting, test design, and reporting

• Preferable: Working knowledge of pytest for test structuring, assertions, and reporting extensions

• Add-on (preferred): Experience with pytest-xray integration for test management and reporting

• Solid knowledge of SQL and query performance tuning

• Preferable: Knowledge of Playwright for browser automation and ability to integrate test execution signals with monitoring tools (e.g., emitting metrics/logs for Dynatrace/AppDynamics/Prometheus/Grafana)

• Bachelors degree in Computer Science, IT, or related field

• Performance testing certification (preferred)

• Experience working in Agile/Scrum environments

• Soft-spoken with clear and professional communication

• Strong team player with collaborative mindset

• Positive attitude and willingness to learn

• Strong analytical and troubleshooting skills

• Effective communication with technical and non-technical stakeholders

• Ability to present performance insights through clear UI dashboards and reports

• Proactive approach to identifying and solving problems

• Adaptability in fast-paced Agile environments

• Ability to translate performance data into actionable engineering decisions

612 Years (Relevant Performance Testing Experience Required)

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Keyskills:   Dynatrace Locust Appdynamics Performance Testing Prometheus databricks Grafana Python