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Hire API integration developers skills in protocols such as REST, GraphQL and SFTP with experience in API Connect and Python/JavaEE, Automation and DevOps. Find API WFH freelancers on January 21, 2025 who work remotely. Read less

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What areas of API development are promoted on HostJane?

API Security:
With the increasing number of cyber threats, API security has become paramount. Areas like OAuth 2.0, JWT (JSON Web Tokens), and API gateways for rate limiting, authentication, and authorization are rapidly evolving.

GraphQL:
GraphQL is gaining traction for its ability to allow clients to request exactly the data they need. It's particularly popular for mobile and web applications where data fetching efficiency is crucial.

Serverless Architectures:
APIs built with serverless technologies like AWS Lambda, Azure Functions, or Google Cloud Functions are on the rise, allowing for highly scalable, cost-effective solutions with less infrastructure management.

Real-Time APIs:
There's a push towards real-time data with technologies like WebSockets, GraphQL subscriptions, or gRPC for streaming data, enabling applications to update in real-time without constant polling.

Machine Learning and AI Integration:
APIs are increasingly being used for exposing machine learning models or AI services, allowing developers to incorporate AI functionalities into their applications without deep AI expertise.

Microservices Architecture:
The trend towards microservices continues, where each service communicates via APIs, requiring robust API design and management skills.

API Management and Lifecycle:
Tools for managing the full lifecycle of APIs, from design to deprecation, are becoming more sophisticated. Platforms like Apigee, Kong, or Azure API Management are pivotal.

Event-Driven Architectures:
APIs are evolving to support event-driven architectures, enabling systems to react to events in real-time through message queues or event streams.

What technical coding skills should someone building my company an API have on their resume?

While practical experience, through projects or contributions to open-source API projects, can be a strong indicator of capability, you should be able to verify that your API developer has direct knowledge of these areas:

1. Programming Languages:

JavaScript/Node.js: For RESTful services, GraphQL APIs.
Python: Popular for backend services, especially with frameworks like Flask or Django for API development.
Java/Go: Common in enterprise environments for scalable backend services.
C#: With .NET Core for cross-platform API development.

2. API Design and Protocols:

REST/HTTP: Understanding of REST principles, HTTP methods, status codes.
GraphQL: Ability to design and implement GraphQL schemas and resolvers.
gRPC: Knowledge for high-performance scenarios where protocol buffers are used.

3. Security:

OAuth 2.0, OpenID Connect, JWT: Skills in implementing secure authentication and authorization mechanisms.
TLS/SSL: Understanding of secure communication over networks.

4. Database Management:

SQL and NoSQL: Familiarity with databases like PostgreSQL, MySQL, or MongoDB for data handling in APIs.

5. API Management:

API Gateways: Experience with tools like Kong, Apigee, or AWS API Gateway.
Documentation: Ability to use tools like Swagger/OpenAPI for API documentation.

6. DevOps and Infrastructure:

Docker, Kubernetes: For containerization and orchestration, especially in microservices environments.
CI/CD: Understanding of continuous integration and deployment pipelines.

7. Asynchronous Programming:

Concurrency and Parallelism: Particularly for handling high loads or real-time data processing.

8. Testing:

Unit, Integration, and Load Testing: Experience with frameworks like Jest, Postman, or specialized API testing tools.

9. Soft Skills:

Communication: To explain technical details to teams and stakeholders.
Problem-Solving: Given the complexities involved in API development.
Collaboration: For working within or across teams, especially in API-first development strategies.

10. Emerging Skills:

Knowledge of Cloud Platforms: AWS, Azure, GCP, especially services like Lambda, Functions, or Cloud Run.
Understanding of Machine Learning APIs: To integrate or expose AI functionalities.

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