Backend Principle Eng Python Pro Max

Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.

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Backend Principle Eng Python Pro Max

Principal-level guidance for Python backend systems in product companies. Emphasizes correctness, reliability, and operational excellence.

When to Apply

Designing or refactoring Python services, APIs, and data pipelines
Reviewing PRs for correctness, reliability, performance, and security
Planning migrations, scalability, or cost optimizations
Incident follow-ups and systemic fixes

Priority Model (highest to lowest)

Priority	Category	Goal	Signals
1	Correctness & Contracts	No wrong answers	Strong validation, invariants, idempotency
2	Reliability & Resilience	Survive failures	Timeouts, retries, graceful degradation
3	Security & Privacy	Zero trust by default	Authz, secrets, minimal data exposure
4	Performance & Efficiency	Predictable latency	Async I/O, bounded queues, caching
5	Observability & Operability	Fast triage	Tracing, metrics, runbooks
6	Data & Consistency	Integrity over time	Safe migrations, outbox, versioning
7	Scalability & Evolution	Safe growth	Statelessness, partitioning, backpressure
8	Developer Experience & Testing	Sustainable velocity	CI gates, deterministic tests, typing

Quick Reference (Rules)

1. Correctness & Contracts (CRITICAL)

api-contracts - Versioned schemas and explicit validation
input-validation - Validate at boundaries, reject unknowns
idempotency - Safe retries with idempotency keys
invariants - Enforce domain rules in service and database
time-utc - Store UTC, use monotonic clocks for durations

2. Reliability & Resilience (CRITICAL)

timeouts - Set per dependency; no unbounded waits
retries - Bounded with jitter; avoid retry storms
circuit-breakers - Fail fast for degraded dependencies
bulkheads - Isolate thread pools and task queues
load-shedding - Graceful degradation under load

3. Security & Privacy (CRITICAL)

authz - Enforce at every service boundary
secrets - Use vault/KMS; never in code or logs
data-min - Redact PII by default
crypto - TLS everywhere; strong defaults
supply-chain - Pin deps; scan CVEs

4. Performance & Efficiency (HIGH)

async-io - Use async for I/O bound paths; avoid blocking
pooling - Right-size DB/HTTP pools; avoid starvation
cache - TTL and stampede protection for hot reads
batching - Batch I/O and DB operations where safe
profiling - Measure before optimizing

5. Observability & Operability (HIGH)

structured-logs - JSON logs with trace ids
metrics - RED/USE metrics plus business KPIs
tracing - Propagate context end-to-end
alerts - SLO-based with runbooks
deploys - Safe rollouts and rapid rollback

6. Data & Consistency (HIGH)

transactions - Clear boundaries; avoid cross-service tx
schema-evolution - Backward compatible migrations
outbox - Reliable event publishing
id-generation - Globally unique IDs
read-models - Use CQRS when complexity is justified

7. Scalability & Evolution (MEDIUM)

stateless - Externalize state, scale horizontally
partitioning - Shard by stable keys
versioning - API and event versioning
backpressure - Bounded queues, explicit limits
config - Dynamic config with validation

8. Developer Experience & Testing (MEDIUM)

typing - Type hints for public APIs and core logic
tests - Unit, integration, contract, load tests
determinism - Hermetic tests, fixed seeds, stable time
lint - Static analysis and formatting

Execution Workflow

Clarify product goals, SLOs, latency and cost budgets
Map data flow, dependencies, and failure modes
Choose storage and consistency model (document tradeoffs)
Define contracts: API schemas, events, and idempotency
Implement with safe defaults, observability, and resilience
Validate with tests, load, and failure scenarios
Review risks and publish runbooks

Language-Specific Guidance

See references/python-core.md for stack defaults, async patterns, and tooling.

---name: backend-principle-eng-python-pro-maxdescription: Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.---# Backend Principle Eng Python Pro MaxPrincipal-level guidance for Python backend systems in product companies. Emphasizes correctness, reliability, and operational excellence.## When to Apply- Designing or refactoring Python services, APIs, and data pipelines- Reviewing PRs for correctness, reliability, performance, and security- Planning migrations, scalability, or cost optimizations- Incident follow-ups and systemic fixes## Priority Model (highest to lowest)| Priority | Category | Goal | Signals || --- | --- | --- | --- || 1 | Correctness & Contracts | No wrong answers | Strong validation, invariants, idempotency || 2 | Reliability & Resilience | Survive failures | Timeouts, retries, graceful degradation || 3 | Security & Privacy | Zero trust by default | Authz, secrets, minimal data exposure || 4 | Performance & Efficiency | Predictable latency | Async I/O, bounded queues, caching || 5 | Observability & Operability | Fast triage | Tracing, metrics, runbooks || 6 | Data & Consistency | Integrity over time | Safe migrations, outbox, versioning || 7 | Scalability & Evolution | Safe growth | Statelessness, partitioning, backpressure || 8 | Developer Experience & Testing | Sustainable velocity | CI gates, deterministic tests, typing |## Quick Reference (Rules)### 1. Correctness & Contracts (CRITICAL)- `api-contracts` - Versioned schemas and explicit validation- `input-validation` - Validate at boundaries, reject unknowns- `idempotency` - Safe retries with idempotency keys- `invariants` - Enforce domain rules in service and database- `time-utc` - Store UTC, use monotonic clocks for durations### 2. Reliability & Resilience (CRITICAL)- `timeouts` - Set per dependency; no unbounded waits- `retries` - Bounded with jitter; avoid retry storms- `circuit-breakers` - Fail fast for degraded dependencies- `bulkheads` - Isolate thread pools and task queues- `load-shedding` - Graceful degradation under load### 3. Security & Privacy (CRITICAL)- `authz` - Enforce at every service boundary- `secrets` - Use vault/KMS; never in code or logs- `data-min` - Redact PII by default- `crypto` - TLS everywhere; strong defaults- `supply-chain` - Pin deps; scan CVEs### 4. Performance & Efficiency (HIGH)- `async-io` - Use async for I/O bound paths; avoid blocking- `pooling` - Right-size DB/HTTP pools; avoid starvation- `cache` - TTL and stampede protection for hot reads- `batching` - Batch I/O and DB operations where safe- `profiling` - Measure before optimizing### 5. Observability & Operability (HIGH)- `structured-logs` - JSON logs with trace ids- `metrics` - RED/USE metrics plus business KPIs- `tracing` - Propagate context end-to-end- `alerts` - SLO-based with runbooks- `deploys` - Safe rollouts and rapid rollback### 6. Data & Consistency (HIGH)- `transactions` - Clear boundaries; avoid cross-service tx- `schema-evolution` - Backward compatible migrations- `outbox` - Reliable event publishing- `id-generation` - Globally unique IDs- `read-models` - Use CQRS when complexity is justified### 7. Scalability & Evolution (MEDIUM)- `stateless` - Externalize state, scale horizontally- `partitioning` - Shard by stable keys- `versioning` - API and event versioning- `backpressure` - Bounded queues, explicit limits- `config` - Dynamic config with validation### 8. Developer Experience & Testing (MEDIUM)- `typing` - Type hints for public APIs and core logic- `tests` - Unit, integration, contract, load tests- `determinism` - Hermetic tests, fixed seeds, stable time- `lint` - Static analysis and formatting## Execution Workflow1. Clarify product goals, SLOs, latency and cost budgets2. Map data flow, dependencies, and failure modes3. Choose storage and consistency model (document tradeoffs)4. Define contracts: API schemas, events, and idempotency5. Implement with safe defaults, observability, and resilience6. Validate with tests, load, and failure scenarios7. Review risks and publish runbooks## Language-Specific GuidanceSee `references/python-core.md` for stack defaults, async patterns, and tooling.

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