Middle/ Senior Python Engineer

ABOUT THE ROLE

Build and evolve Python backend services and event-driven applications on AWS serverless infrastructure. You will own features from design through production, shape engineering standards, and mentor teammates while using AI-assisted development to accelerate delivery. This role combines hands-on coding, cloud architecture, quality ownership, and technical leadership.

RESPONSIBILITIES

• Design, develop, and maintain event-driven microservices and AWS Lambda functions using Python 3.12, Poetry, Pydantic, and AWS Lambda Powertools.
• Build reliable asynchronous integrations across Amazon SQS, EventBridge, and Amazon MSK, including error handling, retries, and idempotency.
• Model, query, and optimize application data in Amazon DocumentDB while maintaining data integrity and service performance.
• Own features throughout their lifecycle, from technical design and implementation to testing, deployment, monitoring, and production support.
• Create unit, integration, and BDD test coverage using pytest and behave, applying SOLID, KISS, DRY, and maintainable design patterns.
• Provision AWS infrastructure with Terraform and maintain GitLab CI/CD pipelines, quality checks, hooks, and automated deployments.
• Establish observability through structured logging, metrics, distributed tracing, dashboards, and alarms in Amazon CloudWatch.
• Review code, mentor engineers, and use GitHub Copilot, reusable instructions, agents, and MCP integrations responsibly to improve delivery.

REQUIREMENTS

• 5+ years of professional Python development experience, including strong knowledge of Python 3 and modern packaging practices such as Poetry.
• Hands-on experience building production services with AWS Lambda and other serverless AWS technologies.
• Strong understanding of event-driven architecture and messaging with SQS, EventBridge, Kafka, or Amazon MSK.
• Experience with document databases, ideally Amazon DocumentDB or MongoDB-compatible technologies.
• Practical knowledge of automated testing with pytest, integration testing, and BDD frameworks such as behave.
• Experience provisioning cloud infrastructure with Terraform and maintaining GitLab CI/CD pipelines.
• Ability to implement production observability using structured logs, metrics, traces, dashboards, and alarms.
• Practical experience using AI coding assistants, agent workflows, or MCP integrations, with disciplined review of AI-generated code.
• Strong ownership, code-review, communication, and mentoring skills, with a focus on secure and maintainable engineering.