Performance Engineer
When to Use
- Profile services under load — CPU, heap, goroutines, GC, lock contention
- Trace requests end-to-end — identify critical path and fan-out
- Load test — baseline, soak, stress, spike; interpret saturation and errors
- Define budgets — p50/p95/p99 targets, error budget, per-dependency ceilings
- Tune databases, caches, queues, and hot code paths with measured before/after
- Detect regressions — benchmark suites, CI gates, release comparisons
- Model capacity — headroom, scaling triggers, cost vs latency trade-offs
- Report findings — reproducible steps, flame graphs, ranked recommendations
When NOT to Use
- Build or fix Jenkins/GitHub Actions pipelines →
devops
- Canary/blue-green rollout plans →
deployment-strategist
- Star schema, dbt layers, warehouse ELT design →
data-warehouse-engineer, analytics-data-engineer
- Implement React components or a11y →
senior-frontend-software-engineer
- General feature delivery without perf focus →
senior-software-engineer
- DC power/cooling/rack utilization programs →
data-center-compute-supply-efficiency
- LLM token benchmarks and compression research →
research-engineer-scientist-tokens
- Enterprise architecture NFR sign-off only →
senior-system-architecture
Related skills
| Need |
Skill |
| Implement fixes in application code |
senior-software-engineer |
| SLO dashboards, alerting, on-call for deploy |
devops |
| Core Web Vitals in product UI code |
senior-frontend-software-engineer |
| Warehouse SQL and partition design |
data-warehouse-engineer |
| Cross-service latency budgets in ADRs |
senior-system-architecture |
| Data pipeline SLA and batch windows |
data-system-ops-lead |
| GPU/cluster capacity for training |
data-center-compute-supply-efficiency |
| Token/cost efficiency experiments |
research-engineer-scientist-tokens |
Core Workflows
1. Profiling and diagnostics
CPU, memory, I/O, contention.
See references/profiling_diagnostics.md.
2. Load and capacity testing
Scenarios, harness, interpretation.
See references/load_capacity_testing.md.
3. Latency, throughput, and SLOs
Percentiles, budgets, error budget.
See references/latency_slo_budgets.md.
4. Database and data-path tuning
Queries, indexes, caches.
See references/database_query_performance.md.
5. Frontend and client runtime
CWV, network, rendering.
See references/frontend_runtime_performance.md.
6. Regression detection and reporting
Baselines, CI, executive summary.
See references/regression_ci_reporting.md.
Outputs
- Perf investigation brief — symptom, scope, hypothesis, reproduction
- Trace + profile pack — flame graph, span waterfall, top offenders
- Load test plan and results — scenario matrix, graphs, bottlenecks
- Optimization backlog — ranked by impact × effort with measurements
- SLO proposal — targets, measurement points, alert thresholds
- Regression report — build-over-build comparison with root cause
Principles
- Measure first — no optimization without baseline and success metric
- One variable — isolate changes; document environment (hardware, data size, version)
- User-centric metrics — tail latency and error rate over averages
- Saturation-aware — distinguish queueing from inefficient work
- Reproducible — scripts, seeds, and configs checked in or attached to report
1---2name: performance-engineer3description: Guides performance engineering—profiling (CPU, memory, I/O), distributed tracing, latency and throughput analysis, load/soak/stress testing, capacity models, performance budgets, database query tuning, and regression detection in CI. Use when investigating slow endpoints, p99 regressions, memory leaks, saturation, flaky load tests, defining latency SLOs, or producing optimization reports with evidence—not for CI/CD pipeline build (devops), rollout cutover strategy (deployment-strategist), warehouse dimensional modeling (data-warehouse-engineer), React/UI implementation (senior-frontend-software-engineer), facility compute utilization (data-center-compute-supply-efficiency), or LLM token/cost research (research-engineer-scientist-tokens).4---56# Performance Engineer78## When to Use910- **Profile** services under load — CPU, heap, goroutines, GC, lock contention11- **Trace** requests end-to-end — identify critical path and fan-out12- **Load test** — baseline, soak, stress, spike; interpret saturation and errors13- **Define budgets** — p50/p95/p99 targets, error budget, per-dependency ceilings14- **Tune** databases, caches, queues, and hot code paths with measured before/after15- **Detect regressions** — benchmark suites, CI gates, release comparisons16- **Model capacity** — headroom, scaling triggers, cost vs latency trade-offs17- **Report** findings — reproducible steps, flame graphs, ranked recommendations1819## When NOT to Use2021- Build or fix Jenkins/GitHub Actions pipelines → `devops`22- Canary/blue-green rollout plans → `deployment-strategist`23- Star schema, dbt layers, warehouse ELT design → `data-warehouse-engineer`, `analytics-data-engineer`24- Implement React components or a11y → `senior-frontend-software-engineer`25- General feature delivery without perf focus → `senior-software-engineer`26- DC power/cooling/rack utilization programs → `data-center-compute-supply-efficiency`27- LLM token benchmarks and compression research → `research-engineer-scientist-tokens`28- Enterprise architecture NFR sign-off only → `senior-system-architecture`2930## Related skills3132| Need | Skill |33|---|---|34| Implement fixes in application code | `senior-software-engineer` |35| SLO dashboards, alerting, on-call for deploy | `devops` |36| Core Web Vitals in product UI code | `senior-frontend-software-engineer` |37| Warehouse SQL and partition design | `data-warehouse-engineer` |38| Cross-service latency budgets in ADRs | `senior-system-architecture` |39| Data pipeline SLA and batch windows | `data-system-ops-lead` |40| GPU/cluster capacity for training | `data-center-compute-supply-efficiency` |41| Token/cost efficiency experiments | `research-engineer-scientist-tokens` |4243## Core Workflows4445### 1. Profiling and diagnostics4647CPU, memory, I/O, contention.4849**See `references/profiling_diagnostics.md`.**5051### 2. Load and capacity testing5253Scenarios, harness, interpretation.5455**See `references/load_capacity_testing.md`.**5657### 3. Latency, throughput, and SLOs5859Percentiles, budgets, error budget.6061**See `references/latency_slo_budgets.md`.**6263### 4. Database and data-path tuning6465Queries, indexes, caches.6667**See `references/database_query_performance.md`.**6869### 5. Frontend and client runtime7071CWV, network, rendering.7273**See `references/frontend_runtime_performance.md`.**7475### 6. Regression detection and reporting7677Baselines, CI, executive summary.7879**See `references/regression_ci_reporting.md`.**8081## Outputs8283- **Perf investigation brief** — symptom, scope, hypothesis, reproduction84- **Trace + profile pack** — flame graph, span waterfall, top offenders85- **Load test plan and results** — scenario matrix, graphs, bottlenecks86- **Optimization backlog** — ranked by impact × effort with measurements87- **SLO proposal** — targets, measurement points, alert thresholds88- **Regression report** — build-over-build comparison with root cause8990## Principles9192- **Measure first** — no optimization without baseline and success metric93- **One variable** — isolate changes; document environment (hardware, data size, version)94- **User-centric metrics** — tail latency and error rate over averages95- **Saturation-aware** — distinguish queueing from inefficient work96- **Reproducible** — scripts, seeds, and configs checked in or attached to report
daemon-blockint-tech