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

Goldman Sachs•Dallas, TX

1d•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

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.
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.
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.