About the role
Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas location_on Dallas, Texas, United States Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas Apply Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas location_on Dallas, Texas, United States Apply WM Data Engineering – Senior Cloud Data Engineer -
Vice President 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 Hands-on Pipeline & Microservices Migration
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. Programming Expert-level proficiency in Java , Python and SQL . 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.
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