Who We Are

Wealth Management

Across Wealth Management, Goldman Sachs helps empower clients and customers around the world to reach their financial goals. Our advisor-led wealth management businesses provide financial planning, investment management, banking, and comprehensive advice to a wide range of clients, including ultra-high net worth and high net worth individuals, as well as family offices, foundations and endowments, and corporations and their employees. Our consumer business provides digital solutions for customers to better spend, borrow, invest, and save. Across Wealth Management, our growth is driven by a relentless focus on our people, our clients and customers, and leading-edge technology, data, and design.

The Client Communications Platform is a strategic initiative establishing industry-leading standards for transparency, efficiency, and consistent service in client and advisor communications. The platform is modernizing through cloud-native infrastructure (AWS) and advanced data processing technologies (Snowflake and Spark) to enhance data quality and availability. Spanning 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 that deliver operational efficiencies and superior client service.

We are seeking a Senior Software Developer with 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 leveraging Spring Boot, Apache Spark, and Snowflake. The ideal candidate combines strong software engineering fundamentals with hands-on cloud expertise, data engineering skills, and a pragmatic approach to reliability, security, and cost efficiency. You are fluent with GitLab for source control, code reviews, and CI/CD.

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 dat
• 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!

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