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Job Description & How to Apply Below
Performance Test Architect
Experience:
10+ Years
Location:
Noida
Mandatory:
Capital Market, Finance
JD:
Required Qualifications
• 10+ years of hands-on experience in performance testing and engineering for large-scale, distributed enterprise applications
• Deep expertise with load testing tools: JMeter, Gatling, and/or k6 — including scripting complex scenarios, correlation, parameterization, and custom plugins
• Strong experience with Kubernetes and Docker: understanding of pod scheduling, resource limits, HPA/VPA, and their impact on application performance
• Proven track record of building performance test frameworks integrated into CI/CD pipelines (Jenkins, Git Lab CI, or Git Hub Actions)
• Hands-on experience with monitoring and observability stacks:
Grafana, Prometheus, ELK (Elasticsearch, Logstash, Kibana), or similar APM tools
• Solid understanding of database performance: SQL tuning, indexing strategies, connection pooling, and behavior under concurrent load (Postgre
SQL, Oracle, or SQL Server preferred)
• Experience with message queue systems (Rabbit
MQ, Kafka, or Active
MQ) and API performance testing (REST/gRPC)
• Proficiency in at least one programming language (Java, Python, or Go) for scripting, tooling, and automation
• Experience leading or mentoring a team of 2+ engineers in performance testing or related disciplines
Preferred Qualifications
• Domain experience in capital markets, financial services back-office operations, or reconciliation systems
• Experience performance testing shared-database architectures with multiple concurrent services
• Familiarity with Infrastructure-as-Code (Terraform, Ansible) and Git Ops practices
• Experience with chaos engineering tools (Litmus, Chaos Monkey) for resilience testing
• Certifications in performance engineering (e.g., ISTQB Performance Testing, AWS/GCP/Azure certifications)
• Experience with financial data volumes: millions of positions/trades per reconciliation cycle
Key Responsibilities
Performance Test Lab & Infrastructure
• Design, build, and maintain a dedicated performance test lab that mirrors production Kubernetes environments (name spaces, resource quotas, network policies, storage classes)
• Provision and manage test infrastructure using Infrastructure-as-Code (Terraform, Helm charts) for repeatable, version-controlled environments
• Set up isolated test databases with realistic data volumes (millions of reconciliation records) and configure MQ/API test harnesses for inter-module communication
• Establish baseline hardware and configuration profiles for consistent benchmarking across releases
Test Strategy & Benchmarking
• Define the comprehensive performance test strategy covering load, stress, soak, spike, capacity, and scalability testing across all modules
• Create workload models based on real production traffic patterns — peak reconciliation windows, batch processing cycles, concurrent user simulations, and MQ throughput scenarios
• Establish KPIs and SLAs: response time percentiles (P50/P95/P99), throughput (TPS), resource utilization ceilings (CPU, memory, IOPS), reconciliation processing rates, and error thresholds
• Design module-level and end-to-end integration performance test suites that validate the shared database model under concurrent multi-module access
• Build automated benchmarking frameworks that produce comparable, versioned performance reports across releases
CI/CD Integration & Shift-Left Performance
• Embed performance test gates into Jenkins/Git Lab CI/Git Hub Actions pipelines so that every build is validated against performance baselines before promotion
• Implement automated regression detection: flag builds that degrade response times or throughput beyond configurable thresholds
• Create lightweight smoke performance tests for PR-level validation and full-scale suite execution for nightly/release pipelines
• Integrate with Grafana/Prometheus/ELK for real-time test execution dashboards, trend analysis, and alerting on performance anomalies
Analysis, Optimization & Collaboration
• Perform deep-dive root cause analysis of performance bottlenecks using profiling tools, APM traces, thread dumps, and database execution plans
•…
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