Asset & Wealth Management-Cloud Architects-Vice President-Dallas

Job Title: WM Data Engineering - Cloud Architect - Software 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 Cloud Architect. The Cloud Architect will serve as the strategic lead for the WM Data Engineering ecosystem. This role bridges the gap between high-level business strategy and hands-on engineering execution, transforming legacy on-premises constraints into scalable cloud-native technical blueprints. You will ensure that the migration of data assets is not only seamless and secure but also architected for long-term accessibility and cost-efficiency in a cloud-first environment.

Key Responsibilities:

1. Strategic Architecture & Design:
   
   
   
   • Cloud-Native Blueprints: Lead the end-to-end architectural design of scalable data platforms using AWS services such as Amazon S3 (Data Lake), AWS Glue, Amazon Redshift, and Amazon Athena.
   • Pipeline Orchestration: Architect automated, resilient ETL/ELT pipelines for both batch and real-time data processing, leveraging AWS Step Functions, Managed Workflows for Apache Airflow (MWAA), or AWS Lambda.
   • Modern Data Patterns: Implement advanced architectural patterns such as Lakehouse to support decentralized data ownership and high-performance analytics across WM business units.
   
   
2. Data Governance & Security:
   
   
   
   • Regulatory Compliance: Ensure all architectures adhere to strict financial regulations (e.g., GDPR, CCPA, SOC2) and internal security standards.
   • Security-by-Design: Implement robust identity and access management (IAM) policies, data encryption at rest and in transit (using AWS KMS), and fine-grained access controls via AWS Lake Formation.
   • Data Quality & Lineage: Design frameworks for automated data quality checks, metadata management, and end-to-end data lineage to ensure "trusted" reporting for wealth advisors and clients.
   
   
3. Cloud Optimization:
   
   
   
   • Cost Management: Drive initiatives by designing cost-effective solutions (e.g., utilizing S3 Intelligent-Tiering, Spot Instances for EMR, and serverless scaling) to maximize ROI on cloud spend.
   • Performance Tuning: Monitor and optimize the throughput of data pipelines and query performance to meet demanding Service Level Agreements (SLAs)
   
   
4. Technical Governance:
   
   
   
   • Set and enforce high standards for code quality, documentation, and testing. Provide mentorship and establish "Golden Paths" or reusable architectural patterns
   • Infrastructure as Code (IaC): Promote a DevOps culture by enforcing the use of Terraform, AWS CDK, or CloudFormation for all infrastructure deployments to ensure consistency and auditability.
   
   
5. Innovation & Modernization:
   
   
   
   • Legacy Migration: Lead the strategy for migrating on-premises data workloads and legacy databases to AWS with minimal business disruption.
   • AI/ML Integration: Architect data foundations that enable seamless integrations for predictive modeling and generative AI applications in wealth management.
   
   

Qualifications:

Technical Requirements

• Experience: 8+ years of progressive experience in Data Engineering or Cloud Architecture, with a proven track record of designing enterprise-scale distributed systems.
• Migration Expertise: Demonstrated success in leading large-scale migrations from on-premises legacy environments to AWS.
• Data Platform Mastery: 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 and senior 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.