Asset & Wealth Management-Cloud Engineer-Associate-Dallas

Goldman Sachs•Dallas, TX

About The Position

WM Data Engineering - Cloud Engineer - Software Engineer - Associate 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 ecosystem. This role focuses on executing technical blueprints and transitioning legacy on-premises constraints into scalable, cloud-native solutions. You will work closely with senior architects to ensure data assets are migrated seamlessly, securely, and cost-effectively.

Requirements

Approximately 5 years of experience in Data Engineering or Cloud Development, with a focus on distributed systems.
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.
Strong problem-solving "builder" mindset and the ability to communicate technical concepts within a team environment.
Bachelor’s or Master’s degree in computer science, Engineering, Mathematics, or a related field.

Responsibilities

Architecture & Design Implementation
Cloud-Native Development: Assist in building scalable data platforms using AWS services such as Amazon S3 (Data Lake), AWS Glue, Amazon Redshift, and Amazon Athena.
Pipeline Development: Develop and maintain automated ETL/ELT pipelines for batch and real-time processing using AWS Step Functions, Managed Workflows for Apache Airflow (MWAA), or AWS Lambda.
Modern Data Patterns: Implement Lakehouse architectural patterns to support high-performance analytics across business units.
Compliance & Security: Apply internal security standards and adhere to financial regulations (e.g., GDPR, CCPA, SOC2). Implement IAM policies, data encryption (AWS KMS), and access controls via AWS Lake Formation.
Data Quality: Execute frameworks for automated data quality checks and maintain metadata management to ensure trusted reporting.
Cost & Performance: Support cost-management initiatives using S3 Intelligent-Tiering and serverless scaling. Monitor pipeline throughput to meet Service Level Agreements (SLAs).
Infrastructure as Code (IaC): Utilize Terraform, AWS CDK, or CloudFormation for consistent infrastructure deployments.
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.