Python software engineers and integration specialists

Python software engineers develop backend applications when business requirements exceed an existing product or an application needs new functionality. Work covers application structure and database design, with automated tests alongside implementation.

• Django development: Build business applications with permission controls and administrative workflows. Extend existing Django codebases without duplicating business rules.
• FastAPI development: Implement typed APIs with request validation and documented contracts. Connect service logic to PostgreSQL through SQLAlchemy where appropriate.

Systems integration specialists connect Python applications to external platforms through REST APIs or message queues. They map business records between systems and define how updates should behave when an upstream service is unavailable.

Work includes authentication and webhook handling, with explicit controls for retries and duplicate messages. For existing integrations, engineers trace failed exchanges and correct mapping errors. Contract tests check compatibility before connected systems change, while reconciliation scripts identify records that need reprocessing.

Python backend developers separate slow processing from interactive requests and replace repetitive manual tasks with scripts that can be maintained alongside application code.

• Asynchronous processing: Implement Celery workers with Redis or RabbitMQ. Define retry limits and idempotent task behavior so interrupted jobs can be restarted safely.
• Scheduled automation: Build file-processing jobs and operational scripts with input validation. Add execution logs and failure alerts so scheduled runs can be checked without inspecting code.

Maintain existing Python applications through production troubleshooting and planned code changes. Engineers reproduce reported faults and add regression tests with pytest before changing affected behavior.

Performance work starts with profiling request paths and database queries, then targets measured bottlenecks such as repeated queries or blocking I/O. Specialists can also update dependencies and replace unsupported Python versions. Where a codebase lacks test coverage, they add tests around affected paths before refactoring, so maintenance can proceed in smaller, reviewable changes.