Senior Software Engineer - Backend
$165k - $180kFitch Group
Fitch Group is currently seeking a Backend Engineer based out of our New York office. As a leading, global financial information services provider, Fitch Group delivers vital credit and risk insights, robust data, and dynamic tools to champion more efficient, transparent financial markets. With over 100 years of experience and colleagues in over 30 countries, Fitch Group’s culture of credibility, independence, and transparency is embedded throughout its structure, which includes Fitch Ratings, one of the world’s top three credit ratings agencies, and Fitch Solutions, a leading provider of insights, data and analytics. With dual headquarters in London and New York, Fitch Group is owned by Hearst. Fitch's Technology & Data Team is a dynamic department where innovation meets impact. Our team includes the Chief Data Office, Chief Software Office, Chief Technology Office, Emerging Technology, Shared Technology Services, Technology, Risk and the Executive Program Management Office (EPMO). Driven by our investment in cutting-edge technologies like AI and cloud solutions, we’re home to a diverse range of roles and backgrounds united by a shared passion for leveraging modern technology to drive projects that matter to our organization and clients. We are also proud to be recognized by Built In as a “Best Place to Work in Technology” 3 years in a row. Whether you're an experienced professional or just starting your career, we offer an exciting and supportive environment where you can grow, innovate, and make a difference. Want to learn more about a career in technology and data at Fitch? Visit: About the Team Join the FDR Data Tech team—a highly visible group responsible for designing, building, and maintaining Fitch's core data repository and ratings platform. The team develops robust Java-based applications and data solutions that power critical business operations across Fitch Ratings. We collaborate closely with stakeholders including Ratings, Business, and Operation teams to deliver scalable data infrastructure, APIs, and services with a focus on reliability, performance, data integrity, and innovation—including the integration of AI/ML capabilities to enhance data solutions and developer productivity How You’ll Make an Impact:
- Design, develop, and maintain scalable, secure, and high-performing backend services and APIs
- Build robust backend services using Java/Spring Boot and/or Python (FastAPI or similar)
- Develop and integrate APIs and data services, ensuring seamless interaction with enterprise data platforms and databases (SQL and NoSQL)
- Collaborate closely with product owners, designers, and data/engineering teams to deliver high-quality, business-aligned solutions
- Contribute to architectural decisions and implement best practices for performance, scalability, and maintainability
- Ensure code quality through automated testing (unit, integration) and active participation in code reviews
- Troubleshoot and resolve complex technical issues, optimizing applications for performance and reliability
- Support CI/CD pipelines and DevSecOps practices to enable efficient and secure deployments
- Contribute to continuous improvement of engineering standards, tooling, and development processes
- Experience building end-to-end applications in cloud-native environments (AWS, Kubernetes/EKS, Docker)
- Experience with event-driven architectures (Kafka or similar)
- Knowledge of caching and high-performance patterns (Redis/Valkey, etc.)
- Exposure to NoSQL databases (e.g., MongoDB)
- Experience implementing CI/CD pipelines (e.g., GitHub Actions)
- Understanding of distributed systems design and data-intensive applications
- Experience working in data-centric platforms (data lakes, APIs, analytics ecosystems)
- Ability to navigate cross-functional environments and influence technical decisions
- Hands-on experience building AI agents (e.g., using LangChain or LlamaIndex), implementing RAG over enterprise data, and enforcing robust guardrails through rigorous testing and evaluation
- Applying AI/ML to boost engineering productivity (e.g., GitHub Copilot, Amazon Q) and using agentic AI to automate workflows such as code reviews, test generation, data quality checks, and runbook execution
- Experience working with AI connection protocols (e.g., MCP, A2A) that ensure secure, interoperable, observable, and vendor-agnostic communication with authenticated, auditable messaging and robust error handling
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