The Core Engineering, Surveillance & Models-Software Engineering, Vice President, Dallas

Goldman Sachs; Dallas (WV); On-site; USD 140,000 - 210,000; Full time

Job summary

Goldman Sachs is seeking a senior software engineer to design end-to-end solutions across divisions. You will partner with sponsors, users, and engineers, leveraging Java, Python, and AI-assisted tools to boost productivity and delivery speed.

The role emphasizes automated testing, SDLC proficiency, and clear communication in meetings and documentation, as you contribute to a broad range of data-centric problems and production-grade software.

Qualifications

• Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field.
• 5+ years professional software development experience.
• Expertise in Java and/or Python development.
• Experience in automated testing and SDLC concepts.
• The ability to clearly express ideas and arguments in meetings and on paper.

Responsibilities

• Partner globally with sponsors, users and engineering colleagues across multiple divisions to create end-to-end solutions.
• Learn from experts and leverage various technologies including Java, Python, SQL, and AI tools.
• Apply analytical and functional skills to design surveillance processes to identify risks.
• Possess initiative to innovate and incubate new ideas and tackle broad data-centric problems.
• Be involved in the full software life cycle: defining, designing, implementing, testing, deploying, and maintaining products.

Skills

Java

Python

Automated testing

SDLC concepts

Communication

Education

Bachelor's or Master's in CS/CE

Tools

GitHub Copilot

Claude Code

Devin

Job description

HOW YOU WILL FULFILL YOUR POTENTIAL

As a member of our team, you will:

• 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, Python, PySpark, SQL, Hadoop, Snowflake, Iceberg, Kafka, Kubernetes
• apply analytical and functional skills to design surveillance processes to identify risks
• 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.

AI‑Assisted Engineering & Productivity

In addition, you will:

• 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.

QUALIFICATIONS

A successful candidate will possess the following attributes:

1. A Bachelor's or Master's degree in Computer Science, Computer Engineering, or a similar field of study.
2. 5+ years professional software development experience
3. Expertise in Java and/or Python development.
4. Experience in automated testing and SDLC concepts.
5. The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.

Experience in some of the following is desired and can set you apart from other candidates:

• Demonstrated experience or a strong interest in leveraging AI‑assistant 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: