Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas

Job Description Who We Look For: Goldman Sachs Engineers are innovators and problem-solvers, building solutions for various divisions. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment. We are seeking a high-caliber, hands‑on Senior Cloud Data Engineer . While you will provide architectural guidance, your primary impact will come from hands‑on engineering : building production‑ready data pipelines, containerizing microservices for Amazon ECS , and executing the technical migration of legacy on‑premises systems to AWS. Key Responsibilities Active Migration Execution: Directly execute the migration of legacy ETL and microservices to AWS. This includes refactoring monolithic code into containerized services and deploying them to Amazon ECS (Fargate/EC2). Containerization & Orchestration: Build and maintain Docker images, write complex ECS Task Definitions, and configure service‑to‑service communication using Amazon ECS Service Connect and AWS Cloud Map. Data Pipeline Engineering: Develop end‑to‑end data flows using AWS Glue (PySpark), Amazon EMR, and Snowflake. Implement "Lakehouse" patterns using Apache Iceberg to ensure data portability. Infrastructure & Automation-as-Code IaC Development: Write and maintain production‑grade Terraform or AWS CDK modules to provision VPCs, ECS clusters, and RDS instances. Ensure all infrastructure is version-controlled and deployed via GitHub Actions or GitLab CI. AI-Augmented Coding: Actively use AI coding assistants (e.g., GitHub Copilot) to refactor legacy SQL, generate unit tests, and automate the creation of boilerplate pipeline code. Toil Reduction: Identify manual bottlenecks in the migration process and build custom automation tools in Python or Go to streamline data validation and schema conversion. Technical Leadership & Reliability Code Reviews & Standards: Lead rigorous peer code reviews, enforcing standards for performance, security (IAM least privilege), and maintainability. Observability Implementation: Hands‑on configuration of Amazon CloudWatch Container Insights, and OpenTelemetry to ensure deep visibility into migrated microservices and data jobs. Performance Tuning: Directly optimize Spark job configurations, Snowflake warehouse sizing, and ECS auto‑scaling policies to balance performance. Qualifications Technical Requirements Experience: 8+ years of hands‑on experience in Data Engineering and Cloud Infrastructure, with a focus on building and migrating production workloads. AWS ECS Expertise: Deep technical expertise in Amazon ECS (Fargate/EC2), including networking (ALB/NLB), task placement strategies, and container security. Data Platform Expertise: Proven experience with modern data platforms such as Snowflake (AI Data Cloud) and cloud-native services. Good understanding of open-source table formats, specifically Apache Iceberg , to enable interoperability, schema evolution, and high-performance analytics across multiple engines. Big Data & Orchestration: Hands‑on experience with Spark, Kafka, and orchestration tools like Apache Airflow, Dagster, or dbt. Data Modeling: Deep understanding of data warehousing and modern data lakehouse architecture. Leadership & Soft Skills Mentorship: Proven track record of upskilling junior engineers. Communication: Ability to explain complex technical concepts to non‑technical stakeholders in the wealth management business. Problem Solving: A "builder" mindset with the ability to navigate ambiguity in a fast-paced environment. Education Bachelor’s or Master’s degree in computer science, engineering, mathematics, or a related field. Goldman Sachs is an equal‑opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law. #J-18808-Ljbffr Goldman Sachs Group, Inc.