Asset & Wealth Management, Business Analytics, Associate - New York

A career with Goldman Sachs Asset & Wealth Management is an opportunity to help clients across the globe realize their potential, while you discover your own. As part of one of the world's leading asset managers with over $2 trillion in assets under supervision, you can expect to participate in exciting investment opportunities while collaborating with talented colleagues from all asset classes and building meaningful relationships with our clients. Working in a culture that values integrity and transparency, you will be part of a diverse team that is passionate about our craft, our clients, and building sustainable success. Bringing together traditional and alternative investments, Goldman Sachs Asset & Wealth Management provides clients around the world with a dedicated partnership and focus on long-term performance. As the primary investment area within Goldman Sachs, we provide investment and advisory services for pension plans, sovereign wealth funds, insurance companies, endowments, foundations, financial advisors and individuals.

Job Duties:

• Architect, build, and maintain scalable, resilient data infrastructure - including data pipelines, ETL/ELT frameworks, and deployment environments - ensuring high availability and performance across multiple platforms.
• Source and integrate large, diverse datasets from multiple origins (APIs, flat files, streaming sources, relational and non-relational databases); transform, cleanse, and normalize data for downstream consumption.
• Design dimensional and analytical data models (star/snowflake schemas, data vault) and load curated data into relational databases, data warehouses, and cloud data platforms.
• Design, develop, and maintain automated, user-friendly reports and dashboards that support the firm's analytical and strategic business needs.
• Generate, distribute, and analyze business-performance metrics for periodic management reporting, translating raw data into actionable insights.
• Prepare and operationalize data assets that power machine-learning models and AI-driven solutions, ensuring data quality, lineage, and governance standards are met.
• Track and report project progress in alignment with the solution delivery lifecycle, proactively managing risks and dependencies.
• Lead stakeholder engagements to identify analytics opportunities, define requirements, and translate business problems into technical solutions.
• Mentor and guide team members, fostering a culture of knowledge-sharing, code reviews, and best-practice adoption.
• Collaborate with solution experts and advisors to showcase outputs, share reusable code, and elevate the team's collective capability.
• Partner with senior leadership on presentations, performance analysis, and future strategy development.
• Devise creative methodologies to improve business efficiency, data reliability, and infrastructure cost-effectiveness across regions and markets.

Required Qualifications:

• Education: Bachelor's or master's degree in computer science, Data Engineering, Information Systems, or a related field.
• Experience: 3+ years of hands-on experience in data engineering, data infrastructure, or a related discipline.
• Cloud Platforms: Proven experience with at least one major cloud provider - AWS (Redshift, Glue, S3, Lambda), Azure (Synapse, Data Factory, ADLS), or GCP (BigQuery, Dataflow, Cloud Storage).
• Programming & Query Languages: Strong proficiency in SQL and Python; familiarity with Scala or Java is a plus.
• Data Modeling: Deep expertise in dimensional modeling (star/snowflake schemas), data vault methodology, and relational database design.
• AI/ML Familiarity: Working knowledge of machine-learning workflows, feature engineering, and model-serving architectures; experience with frameworks like TensorFlow, PyTorch, MLflow, or SageMaker is preferred.
• BI & Visualization: Proficiency with dashboarding tools such as Tableau, Power BI, Alteryx.
• Data Governance: Understanding of data quality frameworks, metadata management, data lineage, and cataloging tools.
• Version Control & CI/CD: Experience with Git, CI/CD pipelines, and infrastructure-as-code practices (Terraform, CloudFormation).