Asset & Wealth Management-Cloud Engineer-Analyst-Dallas

CloudEngineer - Software Engineer - Analyst

Who We Look For:

Goldman Sachs Engineers are innovators and problem-solvers who thrive in fast-paced global environments. We are seeking a motivated Cloud Engineer to support the WM Data Engineering this role you will be a key contributor to the migration and modernization of our on-premises legacy data pipelines and services toAWS. You will work at the intersection of software engineering and data architecture translating technical blueprints into high-performance code. Your mission is to build secure accessible and cost-optimized data assets that power real-time client insights and advanced analytics in a cloud-native environment.

Key Responsibilities:

1. Data Pipeline Development & Migration:
   • ETL/ELT Execution:Build and maintain scalable data pipelines usingAWS GlueAmazon EMR andSnowflake transitioning legacy on-premises workloads to modern cloud-native architectures.
   • Microservices Transition:Execute the migration of on-premises microservices to AWS by containerizing workloads withDockerand deploying them toAmazon ECS.
   • Schema Evolution:Implement and manage open table formats specificallyApache Iceberg to ensure high-performance analytics and seamless schema evolution across the WM data lake.
2. Orchestration & Automation:
   • Workflow Management:Develop and schedule complex data workflows usingApache Airflow (MWAA)orAWS Step Functions ensuring robust error handling and retry logic.
   • Infrastructure as Code (IaC):Deploy and manage cloud infrastructure using TerraformorAWS CDK adhering to the Infrastructure as Code philosophy for all deployments.
   • CI/CD Integration:Maintain automated deployment pipelines to ensure consistent and auditable code promotion across development UAT and production environments.
3. Data Quality & Governance:
   • Automated Testing:Implement data validation to ensure data integrity and accuracy during and after the migration process.
   • Observability:Build monitoring dashboards and alerting mechanisms to track pipeline health data latency and SLA adherence.
4. Modernization:
   • Migration Support:Contribute to the migration of on-premises data workloads to AWS.
   • AI/ML Readiness:Help build the data foundations required for predictive modeling and generative AI applications.

Qualifications:

Technical Requirements

• Experience:2 years of hands-on experience inData EngineeringorSoftware Engineering with a focus on cloud-based data solutions.
• Technical Skills:
  • Experience with modern data platforms like Snowflake and cloud-native AWS services.
  • Understanding of open-source table formats specifically Apache Iceberg.
  • Proficiency in Java Python and SQL.
  • Hands-on experience with Spark Kafka and orchestration tools like Apache Airflow Dagster or dbt.
• Soft Skills:Strong problem-solving builder mindset and the ability to communicate technical concepts within a team environment.

Education

• Bachelors or Masters degree in computer science Engineering Mathematics or a related field.