Production Python Backend Development

Configure Ruff, a static type checker, and pytest as automated quality gates for the project.

Model versioned CI/CD webhook payloads with strict Pydantic v2 validation and explicit handling of unknown fields.

Build a deterministic Python payload generator that emits valid, edge-case, and malformed CI/CD webhook events.

Define API, application, domain, and infrastructure boundaries for a modular-monolith event-processing service.

Normalize provider-specific webhook payloads into immutable domain events using typed adapter interfaces.

Implement an application use case against Protocol-based ports without coupling domain logic to FastAPI or SQLAlchemy.

Select event-analysis rules through a Strategy registry instead of provider-specific conditional chains.

Refactor an AI-generated backend module to remove hidden state, inappropriate inheritance, and unnecessary design-pattern complexity.

Identify blocking operations that would stall an asyncio event loop in a backend request path.

Implement an asynchronous ingestion workflow with explicit timeouts, cancellation handling, and resource cleanup.

Apply bounded concurrency and backpressure to an asynchronous event-processing worker.

Design an exception taxonomy that preserves diagnostic context while separating expected failures from programming defects.

Implement retry behavior with exponential backoff and jitter only for failures classified as transient.

Implement an asynchronous SQLAlchemy 2 repository for domain events stored in PostgreSQL.

Evolve the PostgreSQL schema safely with Alembic migrations, database constraints, and purposeful indexes.

Define transaction boundaries with a unit-of-work abstraction and verify rollback behavior on failure.

Make webhook ingestion idempotent by combining provider event identifiers with database uniqueness guarantees.

Persist processing work with a transactional outbox and consume it through a restart-safe worker.

Test application use cases with pytest fixtures, fakes, and assertions that avoid implementation coupling.

Run asynchronous API and repository integration tests against an isolated PostgreSQL instance in Docker.

Create a focused threat model for the webhook ingestion, query, database, and worker trust boundaries.

Verify webhook authenticity and freshness with constant-time HMAC signature checks and replay protection.

Reject oversized, deeply nested, malformed, or unsupported payloads before they consume significant processing resources.

Enforce JWT-based authentication and scope-based authorization on operational query endpoints.

Load configuration and secrets without hard-coding them, leaking them through logs, or committing them to source control.

Correlate an event across requests and workers using structured logs and OpenTelemetry traces while redacting sensitive data.

Build a minimal multi-stage Docker image that runs as a non-root user and exposes meaningful liveness and readiness checks.

Diagnose a backend bottleneck with load testing, Python profiling, and database query evidence, then verify the improvement.

Deploy the containerized service to Azure Poland Central with supplied Terraform after verifying the forecast cost is within the $50 budget, then destroy all lab resources.

Perform an evidence-based review of an AI-generated backend change for correctness, readability, security, and performance before accepting it.