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

Goldman Sachs•Dallas, TX

About The Position

Goldman Sachs is seeking a high-caliber, hands-on Senior Cloud Data Engineer for their Asset & Wealth Management division. While providing architectural guidance, the primary impact will come from hands-on engineering, including building production-ready data pipelines, containerizing microservices for Amazon ECS, and executing the technical migration of legacy on-premises systems to AWS. The role involves active migration execution, containerization and orchestration, data pipeline engineering, and infrastructure as code development.

Requirements

8+ years of hands-on experience in Data Engineering and Cloud Infrastructure, with a focus on building and migrating production workloads.
Deep technical expertise in Amazon ECS (Fargate/EC2), including networking (ALB/NLB), task placement strategies, and container security.
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.
Expert-level proficiency in Java, Python and SQL.
Hands-on experience with Spark, Kafka, and orchestration tools like Apache Airflow, Dagster, or dbt.
Deep understanding of data warehousing and modern data lakehouse architecture.
Ability to explain complex technical concepts to non-technical stakeholders in the wealth management business.
A "builder" mindset with the ability to navigate ambiguity in a fast-paced environment.
Bachelor’s or Master’s degree in computer science, Engineering, Mathematics, or a related field.

Nice To Haves

Experience containerizing microservices for Amazon ECS.
Experience executing the technical migration of legacy on-premises systems to AWS.
Experience with "Lakehouse" patterns using Apache Iceberg to ensure data portability.
Experience with Infrastructure & Automation-as-Code (IaC) Development using Terraform or AWS CDK.
Experience with AI coding assistants (e.g., GitHub Copilot).
Experience building custom automation tools in Python or Go for data validation and schema conversion.
Experience with Observability Implementation using Amazon CloudWatch Container Insights, and OpenTelemetry.
Proven track record of upskilling junior engineers.

Responsibilities

Directly execute the migration of legacy ETL and microservices to AWS, including refactoring monolithic code into containerized services and deploying them to Amazon ECS (Fargate/EC2).
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.
Develop end-to-end data flows using AWS Glue (PySpark), Amazon EMR, and Snowflake. Implement "Lakehouse" patterns using Apache Iceberg.
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.
Actively use AI coding assistants (e.g., GitHub Copilot) to refactor legacy SQL, generate unit tests, and automate the creation of boilerplate pipeline code.
Identify manual bottlenecks in the migration process and build custom automation tools in Python or Go to streamline data validation and schema conversion.
Lead rigorous peer code reviews, enforcing standards for performance, security (IAM least privilege), and maintainability.
Hands-on configuration of Amazon CloudWatch Container Insights, and OpenTelemetry to ensure deep visibility into migrated microservices and data jobs.
Directly optimize Spark job configurations, Snowflake warehouse sizing, and ECS auto-scaling policies to balance performance.
Upskill junior engineers through mentorship.