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

Dallas, TX, US • Posted 3 hours ago • Updated 3 hours ago

Full Time

On-site

Fitment

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Job Details

Skills

SAP WM
CaliberRM
Code Refactoring
Docker
Extract
Transform
Load
Data Flow
PySpark
Amazon EMR
Terraform
ProVision
Remote Desktop Services
Amazon RDS
GitLab
Continuous Integration
GitHub
Data Validation
IT Management
Microservices
Performance Tuning
Warehouse
Oracle Linux
Data Engineering
IaaS
FOCUS
Migration
Amazon Web Services
Amazon ECS
Amazon EC2
Computer Networking
NLB
Snow Flake Schema
Artificial Intelligence
Cloud Computing
Open Source
Apache HTTP Server
Analytics
Java
Python
SQL
Big Data
Apache Spark
Apache Kafka
Orchestration
Apache Airflow
Data Modeling
Data Warehouse
Leadership
Soft Skills
Mentorship
Communication
Wealth Management
Problem Solving
Conflict Resolution
Computer Science
Mathematics

SAP WM
CaliberRM
Code Refactoring
Docker
Extract
Transform
Load
Data Flow
PySpark
Amazon EMR
Terraform
ProVision
Remote Desktop Services
Amazon RDS
GitLab
Continuous Integration
GitHub
Data Validation
IT Management
Microservices
Performance Tuning
Warehouse
Oracle Linux
Data Engineering
IaaS
FOCUS
Migration
Amazon Web Services
Amazon ECS
Amazon EC2
Computer Networking
NLB
Snow Flake Schema
Artificial Intelligence
Cloud Computing
Open Source
Apache HTTP Server
Analytics
Java
Python
SQL
Big Data
Apache Spark
Apache Kafka
Orchestration
Apache Airflow
Data Modeling
Data Warehouse
Leadership
Soft Skills
Mentorship
Communication
Wealth Management
Problem Solving
Conflict Resolution
Computer Science
Mathematics

Summary

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 10121118
Position Id: 45eed7f0319404d74d6b04a7655ddf66
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