Senior Software Engineer, Global Banking & Markets, Equities Trading Technology

Goldman Sachs•New York, NY

About The Position

Goldman Sachs has embarked on one of its most ambitious engineering programs: the Consolidated Trade Ledger (CTL), a ground-up reimagining of the front-to-back architecture that underpins every trade the firm executes. CTL is a flagship initiative jointly sponsored by Global Markets and Engineering leadership, and it sits at the very heart of the firm's core technology strategy. This new, cloud-native platform will deliver the capacity, extensibility, scalability, and innovation capabilities to power the next two decades of growth for Global Markets, while simultaneously driving significant operational efficiencies. The program follows an engineering-principles-first approach and represents a rare chance to define the foundational systems of a world-leading financial institution from the ground up. As trading volumes continue to accelerate, we are building a small team of ambitious and highly experienced engineers to spearhead the migration of our entire global Equities trading business onto CTL. You will work with a set of core CTL business services and adapt them for Equities Trading, creating capabilities that can be leveraged across Global Markets front, middle, and back office functions. This includes extending CTL's flexible data model to integrate seamlessly with existing production idioms, while meeting the diverse and often complex business requirements of a global 24×7 trading operation, while balancing stringent non-functional demands for availability, latency, and resilience. Critically, you will lean heavily into AI-driven development. Using Goldman Sachs' AI tooling and agentic coding assistants, you will govern multiple AI agents, rapidly comprehend large legacy codebases, generate and assess production-quality code, and accelerate every phase of the software development lifecycle.

Requirements

8+ years of professional software development experience in Java (Java 17+ preferred), with strong command of concurrency, collections, and modern language features
Demonstrated experience with AI-assisted engineering tools (e.g., Claude Code, GitHub Copilot Agent Mode, Devin, Gemini Code Assist), including the ability to govern AI agents, critically assess their output, and maintain quality over AI-generated work product
Experience building event-driven and distributed systems, including familiarity with messaging platforms (e.g., Apache Kafka), delivery guarantees, and resilience strategies
Strong SDLC practices: version control, CI/CD pipelines, automated build/test/deploy workflows, and code quality tooling
Solid testing discipline: unit, integration, and acceptance testing with modern frameworks
Ability to rapidly navigate, understand, and debug large and unfamiliar codebases — with and without AI assistance
Excellent communication and collaboration skills across technical and non-technical audiences in geographically distributed teams

Nice To Haves

Frameworks & Architecture: Spring Boot, gRPC / Protocol Buffers, integration/orchestration frameworks (e.g., Apache Camel, Spring Integration), and pipeline/adapter design patterns (retry, dead-letter queues, error isolation)
Cloud & Infrastructure: Cloud platforms (GCP, AWS), container orchestration (Kubernetes, Docker), and JVM tuning for containerized workloads
Observability & Operations: Application instrumentation (metrics, distributed tracing, structured logging) and production support in high-availability environments
Data & Performance: Data modeling, SQL/NoSQL databases, caching strategies, and performance optimization in latency-sensitive systems
Security: Enterprise security patterns; authentication protocols, mutual TLS, secrets management, and certificate rotation
Domain Knowledge: Equities, post-trade, or financial services experience; trade lifecycle concepts, asset servicing, position management, reconciliation, and multi-system migration environments
Other: Asynchronous / non-blocking I/O frameworks (e.g., Vert.x, Netty), multi-region / BCP architectures, and open-source contribution experience

Responsibilities

Drive a strategic transformation: Work alongside domain experts to understand existing production processes, challenge long-held assumptions that may no longer hold in a cloud-centric, AI-driven world, and define and deliver the requirements for CTL integration.
Multiply your output with AI: Orchestrate AI coding agents (e.g., Claude Code, GitHub Copilot, Devin, Gemini Code Assist) across all stages of the SDLC while maintaining mastery, quality, and production fitness over all AI-generated work product.
Deliver outsized business value: Position Goldman Sachs' Equities business to handle orders-of-magnitude higher volumes at lower operational costs, directly contributing to the firm's competitive and commercial edge.
Build for the future: Design and implement high-availability, multi-region, event-driven services on a modern cloud-native platform, setting the architectural standard for years to come.
Design, build, and operate high-availability, multi-region, cloud-native services with security and comprehensive observability (metrics, distributed tracing, structured logging) built in at every layer.
Develop event-driven architectures, multi-stage processing pipelines, and optimized data paths for high-throughput trade lifecycle management
Partner with engineers, domain experts, and global stakeholders to understand, model, and digitize the firm's business processes, challenging entrenched practices and driving modernization across teams.
Manage the full lifecycle of software components, from requirements analysis through design, development, testing, and release, in line with robust SDLC and Agile best practices.
Innovate creative solutions to complex business and technical problems, building reusable capabilities that can be leveraged across Global Markets front, middle, and back office functions.
Multiply your impact with a modern, AI-centric toolchain, orchestrating AI coding agents across all stages of the SDLC to rapidly comprehend large codebases, generate production-quality code, and accelerate delivery.