Compliance Engineering, Software Engineer, Associate, Dallas

Goldman Sachs•Dallas, TX

About The Position

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. You will learn from experts and leverage various technologies including Java, Python, PySpark, SQL, Hadoop, Snowflake, Iceberg, Kafka, and Kubernetes. You will apply analytical and functional skills to design surveillance processes to identify risks, innovate and incubate new ideas, and have the opportunity to work on a broad range of problems, often dealing with large data sets, including real-time processing, messaging, workflow, and UI/UX. You will be involved in the full life cycle: defining, designing, implementing, testing, deploying, and maintaining software across our products. Additionally, 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. You will apply AI tools to accelerate code generation and refactoring, test creation and coverage improvement, debugging, root-cause analysis, and performance optimization. You will use AI-assisted reasoning to understand complex codebases, rapidly prototype solutions, and improve code quality while maintaining strong engineering standards. You will partner with peers and reviewers to validate, harden, and productionize AI-generated outputs, ensuring correctness, security, maintainability, and regulatory compliance. You will also 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.
3+ years professional software development experience.
Expertise in Java and/or Python development.
Experience in automated testing and SDLC concepts.
The ability (and tenacity) to clearly express ideas and arguments in meetings and on paper.

Nice To Haves

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 API design and micro-services architecture, such as to create interconnected services.
Experience with Cloud-based data platforms, such as Snowflake.
Experience with Apache Spark and/or Hadoop.
Experience with Data Lake or Lakehouse solutions.
Experience with Relational databases.
Knowledge of the financial industry and compliance or risk functions.
Influencing and collaborating with stakeholders.

Responsibilities

Partner globally with sponsors, users, and engineering colleagues across multiple divisions to create end-to-end solutions.
Learn from experts.
Leverage various technologies including Java, Python, PySpark, SQL, Hadoop, Snowflake, Iceberg, Kafka, and Kubernetes.
Apply analytical and functional skills to design surveillance processes to identify risks.
Innovate and incubate new ideas.
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 products.
Effectively use AI-assisted software development tools 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.