Join Goldman Sachs' Engineering Division as a Data, Lakehouse, and AI Data Platform Engineer. This Associate role is central to building scalable systems and transforming data into actionable insights. You will focus on designing, constructing, and maintaining large-scale data platforms, ensuring production pipeline reliability and quality while collaborating closely with platform users.

This is a hands-on delivery role for engineers passionate about building robust production data assets, leveraging modern data technologies, and advancing their careers within the firm. Your contributions will shape data models, pipelines, and platform capabilities essential for analytics, operational decision-making, and innovative AI use cases. You may also enhance platform tooling to meet evolving needs.

As a Data Engineer on the Lakehouse and AI Data Platform team, you will architect, develop, test, and support data pipelines and curated datasets within our advanced data platform. Your responsibilities span ingestion, transformation, modeling, optimization, and data quality assurance, delivering reliable, scalable, and fit-for-purpose data products. You will also contribute to shared tooling and framework components to enhance platform usability and operational efficiency.

Key duties include building and maintaining batch and streaming data pipelines, refactoring existing data flows for improved performance, and developing reusable tooling for enhanced delivery and consistency. Ensuring data pipelines are production-ready, thoroughly tested, and operationally supportable is paramount. You will also develop raw, refined, and curated datasets for analytics and AI, applying sound data modeling principles, and collaborating with consumers to create usable, well-documented data products. Implementing robust data quality controls and reconciliation processes is crucial for building confidence in production outputs.

We seek engineers with at least 3 years of experience, holding a Bachelor's or Master's degree in a relevant field, or possessing equivalent practical experience demonstrating strong quantitative and data engineering skills. Proficiency in Python or Java is required, alongside a solid understanding of SQL, including troubleshooting and optimization. The ability to quickly learn new tools and internal platforms, coupled with familiarity in software engineering fundamentals like version control, testing, and CI/CD practices, is essential.

Candidates should possess a good understanding of temporal data modeling, schema design and evolution, and data performance optimization techniques. A practical approach to data quality, reconciliation, and root-cause analysis is expected. Experience building and supporting production data pipelines, especially with distributed processing frameworks like Apache Spark and common data formats (JSON, Avro, Parquet), is highly valued. Strong ownership of technical design and the ability to guide implementation standards and code quality within a team are also key. Experience leading workstreams, managing dependencies, and supporting junior engineers is advantageous.