These skills do not affect the main grade but show the breadth of your profile.
AI Coding Assistants
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Familiar with AI assistants ChatGPT and Claude. Uses them for generating test scenarios, writing load scripts, and analyzing performance test results.
Effectively applies LLMs for performance tasks: generating JMeter/Gatling scripts from scenario descriptions, analyzing bottlenecks from metrics, creating reports with optimization recommendations.
Defines AI adoption strategy for the performance team: standard prompts for result analysis, AI-assisted test planning, automatic executive report generation.
Familiar with AI-powered IDE Cursor and basic capabilities. Uses autocomplete to accelerate writing JMeter/Gatling/k6 test scripts and load test configurations.
Effectively applies Cursor for performance testing: generating test scripts from scenario descriptions, refactoring complex test scenarios, creating data-driven tests with AI assistance.
Standardizes AI-IDE usage in the performance team: prompt library for common scenarios, configurations for various load testing frameworks, team training.
Uses Copilot for writing test scripts: generating k6 scenarios, data preparation scripts, result analysis code. Critically evaluates suggestions.
Works productively with Copilot: generates complex load test scenarios, Grafana dashboard JSON, analysis scripts. Uses Chat for troubleshooting test results.
Adopts AI in the performance team: automated test script generation, AI-assisted result analysis, bottleneck prediction. Evaluates ML for anomaly detection.
Algorithms & Data Structures
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Understands the fundamentals of Algorithms & Complexity at a basic level. Applies simple concepts in work tasks using Python/Java. Follows recommendations from senior developers when solving problems.
Independently applies algorithmic thinking in performance testing: evaluates load generation algorithm efficiency, selects statistical methods for result analysis, understands percentile calculation algorithms. Analyzes complexity of test data generation and metric correlation approaches.
Establishes algorithm-level performance budgets: latency SLA per endpoint, throughput targets. Reviews load test scenarios for algorithmic efficiency.
Understands the fundamentals of Data Structures at a basic level. Applies simple concepts in work tasks using Python/Java. Follows recommendations from senior developers when solving problems.
Independently selects appropriate data structures for load testing: parameterized data feeds for virtual users, correlation containers for dynamic values, metric collection structures for result analysis. Understands trade-offs between data generation approaches for test realism.
Defines data standards for performance testing: structured test results (time-series), correlation data models, metric aggregation structures. Reviews test script efficiency.
API Management
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Understands the importance of documenting APIs and test scenarios. Can read API documentation to create correct test requests and validate responses in load tests.
Documents performance testing APIs and processes: test scenario descriptions, load parameters, metrics and thresholds. Creates runbooks for conducting standard performance tests.
Defines documentation standards for the performance team: test plan templates, report formats, update policies. Implements docs-as-code for test documentation.
Application Security
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Knows main OWASP Top 10 vulnerability categories. Understands the relationship between security and performance: DDoS attacks, rate limiting, security middleware impact on latency.
Conducts security-oriented performance tests: stress-testing rate limiter and WAF, measuring TLS/encryption overhead, testing resilience against DDoS load patterns.
Defines security performance testing strategy: mandatory security overhead testing per release, standards for conducting load tests securely, compliance checks.
Knows basic secure coding principles: data validation, error handling. Applies secure practices when writing load testing scripts and processing results.
Writes secure performance testing scripts: protecting test credentials, secure storage of load configurations, masking sensitive data in test logs and reports.
Defines security standards for the performance team: test environment access policies, procedures for using production-like data, secure test infrastructure management.
Authentication & Authorization
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Understands JWT/OAuth2 basics for load testing: generating valid tokens for test scenarios, simulating authenticated user sessions at scale, and measuring authentication endpoint performance. Follows team practices for test environment token management and OAuth2 flow testing.
Configures authentication for performance tests: JWT token generation for virtual users, implementing OAuth flows in load scripts, testing token refresh under load.
Defines authentication testing strategy: standard benchmarks for identity infrastructure, performance criteria, recommendations for optimizing the auth layer under load.
Benchmarking
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Understands performance budgets: latency targets per endpoint, memory limits per service, CPU budgets. Monitors budgets during testing.
Creates performance budgets: defines thresholds per service/endpoint, tracks budget consumption. Configures automated alerts on threshold violations. Correlates with business metrics.
Defines performance budget standards: mandatory budgets per service, review process, exception handling. Coordinates budget allocation with product and engineering.
Designs organizational performance budget strategy: business-aligned budgets, cost-performance trade-offs, automated governance. Defines performance culture.
Caching
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Understands caching principles and how Redis works. Knows how caching affects system performance and which Redis metrics matter in performance testing.
Conducts performance testing of systems with Redis: measuring cache hit/miss ratio under load, Redis throughput benchmarks, testing system behavior with cold cache and cache stampede.
Defines cache layer performance testing strategy: standard Redis benchmarks, degradation criteria, sizing recommendations. Consults on optimal caching strategies.
CI/CD
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Runs performance tests in CI: smoke tests per PR, baseline comparison. Reads CI results. Understands when a performance test fails.
Configures CI for performance: automated tests per build, threshold-based pass/fail, results archiving. Integrates k6/Gatling results with PR comments.
Defines CI performance standards: mandatory performance gates, threshold management, reporting. Implements continuous performance testing.
Clean Code & Refactoring
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Understands the fundamentals of Code Quality & Refactoring at a basic level. Applies simple concepts in work tasks using Python/Java. Follows recommendations from senior developers when solving problems.
Independently applies code quality practices in performance testing. Writes clean load test scripts with proper parameterization and correlation handling. Understands trade-offs between test script realism and maintainability. Reviews test scenarios for proper think times, assertion accuracy, and metric collection reliability.
Establishes quality standards for test scripts: code review, version control, reusable libraries. Implements CI for test script validation. Defines naming conventions and documentation.
Cloud Providers
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Uses AWS for performance testing: EC2 for load generators, CloudWatch for baseline metrics. Understands region selection for realistic latency.
Configures AWS for testing: distributed load generation (multi-AZ), S3 for results storage, CloudWatch dashboard integration. Optimizes costs: spot instances for generators.
Defines cloud standards for performance testing: infrastructure provisioning, cost management, environment consistency. Implements infrastructure-as-code.
Code Review
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Participates in test script reviews: checks scenario logic, assertions, data setup. Learns from feedback. Describes PRs with test objectives.
Conducts reviews: checks test realism, metric collection, error handling. Provides feedback on load profiles, assertions, parameterization.
Builds review culture: test script quality checklist, review SLA, automated checks. Trains the team on best practices.
Concurrency & Parallelism
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Understands the fundamentals of Async Programming at a basic level. Applies simple concepts in work tasks using Python/Java. Follows recommendations from senior developers when solving problems.
Independently applies async programming in load testing: concurrent virtual user simulation, async request generation patterns, non-blocking result collection. Understands trade-offs between thread-based and event-driven load generation approaches.
Defines async standards: concurrent virtual users, async data collection, parallel test execution. Implements best practices for non-blocking test scripts.
Containerization
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Runs performance tools in Docker: k6, Grafana, InfluxDB for local testing. Understands container resource limits and their impact on test load.
Configures Docker for performance: containers for load generators, monitoring stack (Prometheus+Grafana), results in InfluxDB. Optimizes container resources.
Defines container standards for performance testing: test agent images, resource guidelines, environment provisioning. Implements container-based test infrastructure.
Database Optimization
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Finds slow queries through load testing: top SQL by duration, execution count. Analyzes EXPLAIN plans. Recommends basic optimizations: indexes, query rewrite.
Profiles query performance: pg_stat_statements analysis, query plan changes under load, parameter sniffing issues. Correlates query performance with application latency.
Defines query performance standards: SLA per query type, mandatory profiling during load tests, automated regression alerting.
Designs query performance optimization strategy: ML-based query analysis, automated tuning recommendations, cross-service query performance governance.
Git & Workflows
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Works with Git for performance scripts: version control for k6/Gatling scenarios, test configs. Follows commit conventions. Writes descriptive commit messages.
Uses Git for performance projects: branch per test scenario, PR review for test scripts. Manages baseline configs, threshold files. Configures git hooks.
Builds Git workflow for the performance team: branching model, review requirements, CODEOWNERS for critical test scenarios.
GraphQL
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Understands basics of GraphQL query performance characteristics — N+1 problems, query depth impact, and resolver execution patterns. Writes simple load tests for GraphQL endpoints using tools like k6 or Artillery. Follows team guidelines for baseline performance benchmarking of GraphQL APIs.
Conducts GraphQL API performance testing: creating realistic queries of varying complexity, testing query depth limits, measuring latency for nested queries and batch operations.
Defines GraphQL performance testing strategy: standard benchmarks, query complexity criteria, optimization recommendations. Develops methodology for the development team.
Infrastructure as Code
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Understands Terraform basics for provisioning load testing infrastructure: test runner instances, target environments, monitoring dashboards. Can use existing configurations to spin up and tear down test environments. Follows IaC practices for reproducible test setups.
Uses Terraform for performance infrastructure: deploying load generators in the cloud, setting up test environments with required topology, managing resources for distributed load testing.
Defines IaC strategy for the performance platform: reusable modules for various scenarios, cost management for test infrastructure, GitOps for environment management.
Integration Testing
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Understands the role of integration testing in the performance context. Knows how integration tests help identify performance bottlenecks at component boundaries.
Develops integration performance tests: testing end-to-end latency across service chains, verifying inter-service communication throughput, database bottleneck detection.
Defines integration performance testing standards: mandatory scenarios per release, pass/fail criteria, escalation processes when component-level degradation is detected.
Kubernetes & Orchestration
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Runs performance tests in K8s: load generator pods, port-forwarding for target access. Monitors K8s metrics during tests. Reads pod logs for debugging.
Configures K8s for performance testing: Job resources for test runs, resource requests for consistent results. Monitors K8s cluster performance during tests.
Defines K8s standards for performance: test environment templates, resource policies, scheduling. Coordinates shared test infrastructure.
Designs K8s performance testing platform: multi-cluster test execution, self-service test environments, automated capacity planning.
Logging
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Analyzes structured logs for performance: filters by duration, status code. Correlates log entries with test scenarios. Understands timing fields in logs.
Designs performance logging: timing fields (request_duration, db_query_time), resource usage context. Configures log-based alerting for performance degradation.
Defines performance logging standards: mandatory timing fields, log-based SLI calculation, analysis procedures.
Metrics & Monitoring
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Monitors performance via Grafana: request rate, latency percentiles, error rate, resource utilization. Creates dashboards for load test results.
Configures performance monitoring: custom metrics (latency histograms, throughput gauges), recording rules for SLI, alerting on performance degradation. Load test result dashboards.
Defines performance metrics standards: mandatory metrics, dashboard templates, threshold management. Implements SLO-based performance monitoring.
OOP & Design Patterns
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Knows fundamental design patterns: Factory, Strategy, Observer. Understands how patterns apply when creating test scripts and load generators in performance projects.
Applies patterns in load testing tools: Strategy for different load models (ramp-up, spike, soak), Builder for constructing scenarios, Observer for metric collection.
Defines architectural patterns for the performance platform: standard solutions for distributed load generation, scaling patterns for test infrastructure, reporting templates.
Understands the fundamentals of OOP & SOLID Principles at a basic level. Applies simple concepts in work tasks using Python/Java. Follows recommendations from senior developers when solving problems.
Independently applies OOP/SOLID in load testing frameworks: proper abstraction for virtual user scenarios, interface-based protocol handlers, single responsibility in data feeders and result collectors. Understands trade-offs between OOP patterns and DSL approaches in performance testing tool design.
Defines code standards for performance tests: modular test scripts, reusable components (Page Objects for load tests), abstract base classes for different protocols.
Profiling
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Profiles CPU: uses top/htop for monitoring, flame graphs for hotspot analysis. Understands user vs system time, context switches. Identifies CPU-bound bottlenecks.
Conducts CPU profiling: perf/async-profiler for JVM, py-spy for Python, CPU flame graph analysis. Identifies: hot methods, lock contention, GC overhead. Correlates with load test results.
Defines CPU profiling standards: mandatory profiling during load tests, analysis procedures, optimization tracking. Implements always-on profiling.
Designs profiling platform: fleet-wide continuous profiling, automated hotspot detection, cost-performance correlation. Defines profiling strategy.
Prompt Engineering
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Understands basic principles of working with AI models. Formulates prompts to LLMs for help with writing test scripts, analyzing logs, and interpreting test results.
Applies prompt engineering for performance tasks: generating load scenarios from user stories, automated test result analysis, creating reports and optimization recommendations.
Defines AI strategy for the performance team: standard prompts for common analyses, LLM integration into reporting, automating recommendation generation from test results.
REST API
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Tests API performance: baseline latency (p50, p95, p99), throughput (RPS), error rates. Uses Postman/curl for smoke tests. Understands HTTP keep-alive impact.
Designs API performance tests: realistic user scenarios, ramp-up patterns, peak load simulation. Tests: pagination performance, payload size impact, compression benefits.
Defines API performance standards: mandatory performance testing per endpoint, SLA definitions, regression gates in CI. Coordinates performance requirements with product.
System Design
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Understands basic system design concepts: client-server, load balancers, caches, databases. Knows how system architecture affects its performance.
Applies system design knowledge when planning performance tests: identifying load points, modeling user workload, designing test environments that mimic production.
Defines performance testing approaches for various architectural patterns: serverless, CQRS, event sourcing. Advises architects on performance implications of their decisions.
Type Systems
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Understands type safety concepts and their importance for code reliability. Uses typing when writing test scripts to prevent errors in load parameters.
Applies strict typing in performance scripts: TypeScript for k6 tests, typed load configurations, interfaces for test results and metrics, test parameter validation.
Defines type safety standards for the performance team: mandatory script typing, type-safe configurations, code review criteria for preventing runtime errors in tests.
Unit Testing
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Understands unit testing principles and its role in quality assurance. Writes basic tests for utilities and helpers used in load testing scripts.
Develops unit tests for performance testing components: validating load generators, verifying metric parser correctness, testing custom plugins for JMeter/Gatling.
Defines testing standards for the load testing team: script coverage, mandatory tests for custom extensions, CI integration for validating test scenarios before execution.