Opportunity brief
Goldman Sachs is hiring for the role of Compliance Analyst!
Responsibilities of the Candidate:
- partner globally with sponsors, users and engineering colleagues across multiple divisions to create end-to-end solutions,
- learn from experts
- leverage various technologies depending on the team including Java, JavaScript, TypeScript, React, APIs, GraphQL, Elastic Search, Kafka, Kubernetes
- be able to innovate and incubate new ideas
- have an opportunity to work on a broad range of problems, often dealing with large data sets, including real-time processing, messaging, workflow and UI/UX
- be involved in the full life cycle; defining, designing, implementing, testing, deploying, and maintaining software across our products.
- Effectively use AI‑assisted software development tools (e.g., GitHub Copilot, Devin, Claude Code, or equivalent) to improve developer productivity and reduce development cycle time.
- Apply AI tools to accelerate:
- code generation and refactoring,
- test creation and coverage improvement,
- debugging, root‑cause analysis, and performance optimization.
- Use AI‑assisted reasoning to understand complex codebases, rapidly prototype solutions, and improve code quality while maintaining strong engineering standards.
- Partner with peers and reviewers to validate, harden, and productionize AI‑generated outputs, ensuring correctness, security, maintainability, and regulatory compliance.
- Identify opportunities where AI tooling can reduce manual effort, minimize rework, and support faster, higher‑quality delivery to production
Requirements:
- A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field of study.
- 1+ years of professional software development experience
- Expertise in Java development.
- Experience in automated testing and SDLC concepts.
- The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.
Desired Qualification:
- Demonstrated experience or a strong interest in leveraging AI-assisted developer tools to enhance engineering productivity and accelerate software delivery.
- Ability to critically evaluate AI‑generated outputs and enhance them to production‑grade quality.
- Proven ability to critically evaluate AI-generated outputs and refine them to meet production-grade standards of quality, reliability, and maintainability.
- Hands-on proficiency with AI coding assistants such as GitHub Copilot, Claude Code, and Devin to expedite code authoring, refactoring, and debugging across all phases of the Software Development Life Cycle (SDLC).
- Effective utilization of AI pair-programming tools to generate boilerplate code, unit tests, and technical documentation, enabling greater focus on architectural design and complex problem-solving.
- Application of established prompt engineering best practices to elicit high-quality, context-aware code recommendations and reduce iteration cycles during development.
- Practical integration of autonomous AI agents (e.g., Devin) for routine engineering tasks, including bug triage, dependency upgrades, and minor feature implementations, thereby freeing engineering bandwidth for higher-impact initiatives.
- Adoption of AI-driven code review tools to proactively identify defects, security vulnerabilities, and performance bottlenecks earlier in the development cycle, strengthening overall quality gates.
- Experience with:
- UI/UX development
- API design, such as creating interconnected services
- message buses or real-time processing
- relational databases
- Knowledge of the financial industry and compliance or risk functions
- influencing and collaborating with stakeholders.