Salary
≈ $49k – $110k per year (Estimated)
Location
In office (Mexico)
Seniority
Senior · 2+ years exp
Confirmed on the employer's own hiring board on Oct 10, 2026. First seen by Alion on Oct 9, 2026. Chubb scores A on the Alion truth index.
Overview
Company
Impact
Profile match
Chubb is the largest publicly traded property and casualty insurer in the world, formed in 2016 when ACE acquired the older Chubb Corporation and took its name. It writes commercial property and casualty, specialty lines, high net worth personal insurance, accident and health cover and reinsurance across more than fifty countries, and it is known in the industry for underwriting discipline and for walking away from business it considers underpriced. Headquartered in Zurich with major operations in New York and Philadelphia, it is listed on the New York Stock Exchange.
About the Role
We are seeking a Software Engineer with solid backend experience to design and deliver production-grade features end-to-end. You will operate with meaningful autonomy on moderately complex problems, take ownership of quality, and begin shaping technical decisions within your squad - including how AI tools are used responsibly across the team.
Responsibilities
- Design and implement backend services and APIs using both Java (Spring Boot / Quarkus) and Python (FastAPI), selecting the appropriate language and framework for the problem at hand
- Apply object-oriented design with confidence: design class hierarchies, favor composition over inheritance, apply SOLID principles, and refactor code that violates them
- Select and apply design patterns appropriately - Adapter, Repository, Observer, Decorator, Template Method, and others - and articulate the trade-offs of each choice in code reviews
- Design and document RESTful APIs: resource modeling, versioning strategies, pagination, error schemas, and authentication via OAuth 2.0 / JWT
- Write intermediate to advanced SQL Server queries: window functions, CTEs, stored procedures, index strategies, and execution plan analysis for optimization
- Implement Kafka producers and consumers in production services: manage consumer groups, handle offset commits, design for idempotency, and deal with deserialization errors
- Deploy and configure Azure services: Azure Kubernetes Service (AKS), Azure Key Vault, and Azure AppInsights; understand how these map to cloud-native patterns
- Manage Git workflows at the team level: enforce branching conventions, conduct meaningful code reviews, resolve complex merge conflicts, and use rebase and cherry-pick confidently
- Integrate AI development tools (Claude Code) into daily workflows - using them for design exploration, test generation, code review assistance, and documentation - while enforcing quality gates on all AI-generated output
- Mentor junior developers through pair programming, structured feedback, and design walkthroughs
- Deliver accurate estimates, communicate risks proactively, and own feature quality end-to-end
Required Skills & Experience
- 2-5 years of professional backend software development experience
- Proficiency in both Java (Spring Boot / Quarkus) and Python (FastAPI)
- Solid command of OOP design and SOLID principles with demonstrable application
- Practical knowledge of common design patterns and the ability to recognize and correct anti-patterns
- Strong RESTful API design skills including versioning, error handling, and security
- Intermediate SQL Server skills: query optimization, indexing, transactions, and schema design
- Production experience with Kafka: consumers, producers, consumer groups, and offset management
- Hands-on experience deploying to Azure (AKS, Azure Key Vault, Azure AppInsights, or equivalent)
- Fluent in team Git workflows including branching models (GitFlow or trunk-based) and pull request best practices
- Demonstrated use of AI coding assistants to improve productivity without compromising code quality
Nice to Have
- Experience with Azure DevOps pipelines for CI/CD
- Knowledge of Kafka Streams or schema registry
- Exposure to API gateway patterns
- Contributions to internal libraries or shared tooling
- Experience authoring AI tool usage guidelines for a team
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