What are the responsibilities and job description for the Asset & Wealth Management-Cloud Architects-Vice President-Dallas position at The Goldman Sachs Group?
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:
- 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.
- 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.
- 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)
- 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.
- 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.
ABOUT GOLDMAN SACHS
At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
The Goldman Sachs Group, Inc., 2023. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
The Goldman Sachs Group, Inc., 2018. All rights reserved Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Vet.