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Financial Services, Ratings Agency, Data & Analytics

Sr. Fullstack Engineer (Python)

New York, New York, United StatesHybridContractSenior$50–$50 /hrPosted 2 months agoVisa sponsorship available

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Job Title - Sr. Fullstack Engineer (python)

Location – NY-Hybrid role (three days a week in office).

Rate – Avenue Code W2: $50 (6 months with possibility of extension)

Responsibilities:

Develop, test, and maintain scalable Python applications.

Collaborate with product managers, designers, and other engineers to deliver high-quality software.

Write clean, efficient, and reusable code following best practices.

Participate in code reviews to ensure code quality and share knowledge with the team.

Troubleshoot and debug issues in a timely manner.

Contribute to the design and architecture of new features and systems.

Have a sense of ownership and craftsmanship around the code base and your work.

Enjoy helping other developers grow and learn new technologies.

Display a strong track record of mentorship with engineers at various levels.

Are mindful of application security and performance.

Take pride in learning, and want opportunities to learn throughout your day-to-day.

Possess a pragmatic mindset.

✅ Requirements:

3–6 years of professional software engineering experience, with a strong portfolio of full stack development work.

Proficiency in Python, including experience with web frameworks such as Flask.

Cloud experience, particularly with AWS (Amazon Web Services).

Experience integrating frontend applications with RESTful APIs and backend services.

Relational and non-relational databases (SQL Server, Snowflake and MongoDB)

Debugging, issue resolution, and troubleshooting.

🚀 Nice to have skills:

Familiarity with UX design tools (Figma) and solid understanding of the design-engineering hand-off process

Containerized development and deployment (i.e. Docker, Docker swarm, Kubernetes)

Infrastructure as Code (Terraform)

Familiarity with deployment pipelines, CICD tools.

Exposure to financial systems or credit modeling is strongly preferred.

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