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

Goldman Sachs • Dallas, TX

8d • Onsite

About The Position

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.

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, to enable interoperability, schema evolution, and high-performance analytics across multiple engines.
• 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.
• Proven track record of upskilling junior engineers.
• 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.

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 to ensure data portability.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior