Software Engineer Associate

Software Engineer - Associate – Asset & Wealth Management Engineering

Who We Are

Wealth Management at Goldman Sachs empowers clients globally to achieve their financial goals. Our businesses offer financial planning, investment management, banking, and advice to a diverse clientele, including high-net-worth individuals, family offices, foundations, endowments, and corporations. Our consumer division provides digital tools for spending, borrowing, investing, and saving. Growth is fueled by a commitment to our people, clients, and cutting-edge technology, data, and design.

The Client Communications Platform is a key initiative focused on setting industry-leading standards for transparency, efficiency, and consistent client and advisor communication. This platform is undergoing modernization with cloud-native infrastructure on AWS and advanced data processing technologies like Snowflake and Spark. This enhances data quality and availability across five technical domains: data sourcing, content generation, storage and accessibility, client delivery, and workflow analytics. The platform automates processes, improves user experiences, and transitions from legacy systems to modern, scalable solutions for operational efficiency and superior client service.

We are looking for a Senior Software Developer with over 5 years of experience to design, build, and operate cloud-native, data-intensive systems on AWS. You will lead the development of resilient microservices and high-throughput data pipelines using Spring Boot, Apache Spark, and Snowflake. The ideal candidate possesses strong software engineering fundamentals, hands-on cloud expertise, data engineering skills, and a practical approach to reliability, security, and cost efficiency. Fluency with GitLab for source control, code reviews, and CI/CD is essential.

Your Impact

• Design, develop, and own cloud-native microservices and data pipelines on AWS using Spring Boot, Apache Spark, and Snowflake.
• Build RESTful and event-driven services with Spring (Boot, Data, Cloud), integrating with Snowflake for analytical and operational data use cases.
• Implement batch and streaming data processing using Spark (DataFrames, Spark SQL, Structured Streaming) on EMR or EKS; orchestrate with AWS Glue, Step Functions, or Airflow.
• Model and optimize Snowflake workloads (virtual warehouses, micro-partitioning, clustering, query profiling, caching); implement Snowpipe, Tasks, Streams, RBAC, and data masking.
• Apply AWS Well-Architected best practices across reliability, security, performance, cost optimization, and operational excellence (VPC design, IAM least privilege, KMS, Secrets Manager, CloudWatch).
• Implement observability and SRE practices: metrics, logs, tracing (OpenTelemetry), dashboards (CloudWatch, Grafana), alerting, SLOs, incident response, and postmortems.
• Perform performance engineering (API latency, P99 improvements, Spark job tuning, Snowflake warehouse sizing) and cost governance (right-sizing, auto-suspend, lifecycle policies).
• Collaborate closely with product, data, and platform teams; author technical designs, review merge requests, and mentor engineers.
• Uphold high standards for testing (unit, integration, contract, performance), code quality, and secure coding; leverage GitLab pipeline gates for quality and security checks.

Basic Qualifications

• 3-5 years of professional software engineering experience building production systems.
• Strong Java with Spring Framework (Spring Boot, Spring Data; Spring Cloud preferred); familiarity with Scala is a plus.
• 3+ years of hands-on AWS experience with core services: EC2, S3, IAM, VPC, RDS/Aurora, Lambda, ECS/EKS, CloudWatch; understanding of networking (subnets, routing, security groups, NACLs).
• 2+ years working with Apache Spark (DataFrames, Spark SQL, Structured Streaming) including performance tuning (partitioning, join strategies, memory management, serialization).
• 2+ years of Snowflake experience (SQL, schema design, virtual warehouses, Tasks, Streams, Snowpipe, RBAC) with demonstrated query optimization and cost control.
• Solid grasp of cloud computing and distributed systems fundamentals: scalability, availability, consistency tradeoffs, event-driven architectures, idempotency, and backpressure.
• Proficient in data modeling and building reliable data integrations and APIs.
• Experience with CI/CD using GitLab CI/CD and Git version control; strong familiarity with merge requests, code reviews, and branching strategies.

Preferred Qualifications

• AWS Certifications (Solution Architect, Developer Associate/Professional)
• Spring Cloud patterns (service discovery, config, circuit breakers via Resilience4j).
• Microservices and domain-driven design (DDD) and API versioning.
• Observability stack: OpenTelemetry, Prometheus, Grafana; distributed tracing.
• Infrastructure as Code with Terraform.
• Security and compliance familiarity (SOC 2, ISO 27001, GDPR) and secure data handling (PII, tokenization, masking).
• Cost optimization strategies in cloud data platforms and Spark.

Goldman Sachs Engineering Culture

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here!

© The Goldman Sachs Group, Inc., 2023. All rights reserved.

Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Veteran/Sexual Orientation/Gender Identity

Key skills/competency

• Software Engineering
• Cloud-Native Development
• AWS
• Spring Boot
• Apache Spark
• Snowflake
• Microservices
• Data Pipelines
• CI/CD
• GitLab