Job Description

What We Do

At Goldman Sachs, our Engineers don’t just make things – we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.

Who We Look For

Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

Job Summary: The Senior Data Engineer / Data Architect will serve as a technical leader and subject matter expert, responsible for defining, designing, and overseeing the implementation of enterprise-level data strategies, architectures, and solutions. This role demands extensive experience in managing complex data environments, a strategic mindset to align data initiatives with business objectives, and the ability to lead, mentor, and influence cross-functional teams to foster a data-driven culture. The incumbent will ensure data integrity, accessibility, security, and compliance across the organization's data assets.

Key Responsibilities

Strategic Data Architecture & Roadmapping: Lead the development and execution of the organization's overarching data strategy and architectural roadmap, including data governance frameworks, data modeling, data warehousing, data lakes, and real-time data platforms.
Complex Data System Design & Implementation: Architect and design highly scalable, robust, and fault-tolerant data pipelines and platforms for large-scale data ingestion, processing (batch and real-time), storage, and consumption. This includes selecting appropriate technologies and patterns (e.g., data mesh, data fabric, zero-ETL).
Data Governance & Quality Leadership: Define and implement comprehensive data governance policies, standards, and best practices to ensure data quality, consistency, security, privacy, and regulatory compliance (e.g., GDPR, HIPAA). This includes designing and maintaining data catalogs and metadata management.
Advanced Data Modeling & Database Design: Lead the design and maintenance of conceptual, logical, and physical data models for various data stores, optimizing for performance, scalability, and flexibility.
Technology Evaluation & Adoption: Continuously evaluate emerging data technologies, tools, and industry trends (e.g., open table formats, AI-powered data tools) to recommend and lead their adoption, driving innovation and efficiency within the data landscape.
Performance Optimization & Scalability: Proactively identify and resolve complex data-related performance bottlenecks, ensuring optimal performance and scalability of data platforms and solutions to support growing data volumes and analytical demands.
Technical Leadership & Mentorship: Provide expert technical guidance, mentorship, and leadership to data engineering and analytics teams, fostering best practices in data architecture, development, and operational excellence.
Cross-Functional Collaboration & Stakeholder Management: Collaborate extensively with business stakeholders, product teams, software engineers, and IT operations to translate complex business requirements into effective data solutions and ensure alignment with organizational goals.
Incident Response & Troubleshooting: Oversee the resolution of critical data-related incidents, perform root cause analysis, and implement preventative measures to ensure high availability and reliability of data systems.

Qualifications

Experience: 14+ years of progressive experience in Data Engineering, Data Architecture, or related roles, with a strong focus on designing and implementing large-scale, enterprise-level data solutions.
Education: Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, or a related quantitative field.
Cloud Platforms: Expert-level proficiency and extensive hands-on experience with major cloud providers (e.g., AWS, GCP, Azure) and their data services (e.g., AWS S3, Redshift, Glue, EMR; GCP BigQuery, Dataflow, Dataproc; Azure Data Lake, Synapse, Databricks).
Big Data Technologies: Deep expertise in big data technologies such as Apache Spark, Hadoop, Kafka, and distributed processing systems.
Data Warehousing & Data Lakes: Proven experience with data warehousing (e.g., Snowflake, Redshift, BigQuery, Synapse Analytics) and data lake/lakehouse architectures (e.g., Delta Lake, Azure Data Lake Storage).
Programming & Scripting: Strong programming skills in languages commonly used in data engineering (e.g., Python, Scala, Java, SQL).
ETL/ELT & Data Integration: Extensive experience in designing, building, and optimizing complex ETL/ELT pipelines and data integration processes.
Data Modeling: Mastery of various data modeling techniques (e.g., dimensional modeling, relational modeling) and tools.
Databases: Strong understanding of various database technologies (SQL and NoSQL) and data platforms, especially in high-performance, high-availability contexts.
Infrastructure as Code (IaC): Familiarity with IaC tools (e.g., Terraform, CloudFormation, Ansible) for managing data infrastructure.
DevOps/DataOps/MLOps: Understanding and experience with DevOps, DataOps, and MLOps principles and practices for automated data and model deployment.
Communication & Leadership: Exceptional communication, presentation, and interpersonal skills, with a proven ability to influence stakeholders at all levels and lead technical teams.

Preferred Qualifications

Experience in specific industry domains (e.g., finance, healthcare, e-commerce).
Certifications in relevant cloud data platforms (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer, Azure Data Engineer Associate).
Experience with data visualization tools (e.g., Tableau, Power BI).
Familiarity with master data management (MDM) and data quality tools.
Understanding of machine learning workflows and MLOps.

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.

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