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

WM Data Engineering Senior CloudData 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-onSenior Cloud Data Engineer. While you will provide architectural guidance your primary impact will come fromhands-on engineering: building production-ready data pipelines containerizing microservices forAmazon ECS and executing the technical migration of legacy on-premises systems to AWS.

Key Responsibilities:

1. 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.
2. 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.
3. 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 PlatformExpertise:Proven experience with modern data platforms such asSnowflake(AI Data Cloud) and cloud-native services. Good understanding of open-source table formats specificallyApache Iceberg to enable interoperability schema evolution and high-performance analytics across multiple engines.
• Programming:Expert-level proficiency inJava PythonandSQL.
• 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

• Bachelors or Masters degree in computer science Engineering Mathematics or a related field.