Job description

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.

Requirements

Must-haves

WM Data Engineering – Senior Cloud Data Engineer - Vice President
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.
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
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.
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.
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.
Bachelor’s or Master’s degree in computer science, Engineering, Mathematics, or a related field.

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Keywords to mirror

awsdockeraisqlpythongojava

Platform: goldman-sachs
Effort: medium

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