Vice President - Core Engineering (Data Architect)

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

Preferred Qualifications

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.

Skills Required

Java, Hadoop, Scala, Apache Spark, Kafka, Sql, ELT, Devops, Gcp, MLops, Data Warehousing, Azure, Python, Etl, Aws