Senior Backend Engineer - Python & Databricks

JobgetherBackend software

Based in BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Experience: At least 4 years of backend development experience
Required Skills: DockerPythonSQLMicrosoft AzureFastAPICI/CDRESTful APIsDatabricks

Have at least 4 years of backend development experience, including building, integrating, and operating production services.
Demonstrate strong proficiency in Python and experience developing REST APIs with FastAPI or equivalent frameworks.
Design modular, maintainable software using principles such as SOLID and separation of concerns.
Have experience with automated testing best practices.
Have experience integrating APIs and distributed systems, including authentication, timeouts, error handling, retries, and resiliency patterns.
Have experience with asynchronous processing and messaging systems, including queues, dead-letter handling, and idempotency.
Bring strong SQL and relational database skills, including data modeling, querying, migrations, and ORM usage.
Have experience with Docker and deploying containerized services.
Have hands-on experience with cloud services, preferably Microsoft Azure, as well as monitoring, Git version control, and CI/CD processes.
Experience with Azure Service Bus or equivalent technologies is relevant; SQL Server or Azure SQL experience is preferred.
Experience with AI-assisted development tools or Spec-Driven Development, Azure Databricks or similar platforms, and Kubernetes/AKS or Azure Container Registry is a plus.

Design, develop, and evolve backend services and REST APIs using Python and FastAPI.
Build and operate a communications dispatch service integrating messaging queues and external APIs.
Implement idempotency, retries, rate limiting, and robust error handling.
Integrate and orchestrate Azure services, relational databases, third-party APIs, and data processes such as Databricks.
Design and maintain Azure SQL persistence solutions, including data modeling, migrations, data integrity, and query optimization.
Improve production reliability and observability through monitoring, tracing, automated testing, and failure recovery.
Collaborate with development, data, and IT/DevOps teams on technical decisions, integrations, CI/CD pipelines, and deployments.
Maintain clear and useful technical documentation.

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