Location
London

Hours
Full Time

Salary
Negotiable

About the Role
Join a team building the data foundations that support the firm’s AI and analytics capabilities. This role is part of the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm. At Goldman Sachs, engineering teams are central to the business, building scalable systems, solving complex technical problems and turning data into action.

As a Data Engineer in the Lakehouse and AI Data Platform team, you will design, build, test and support data pipelines and curated datasets on the firm’s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. You may also contribute to shared tooling or framework components to improve platform functionality.

This delivery-focused role suits engineers who want to build robust data assets in production, work with modern data technologies, and grow within the firm. You will contribute to data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases.

Key Responsibilities
- Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
- Refactor or modernise existing data flows to improve reliability, performance and maintainability.
- Develop raw, refined and curated datasets supporting analytics, reporting and AI use cases.
- Apply sound data modelling principles to represent business entities, relationships and historical change.
- Implement controls to validate completeness, accuracy and consistency of data.
- Work closely with engineers, platform teams and data consumers to deliver agreed outcomes.
- Contribute to testing, monitoring, reconciliation tooling and platform reliability improvements.
- For senior candidates, provide technical leadership, task breakdown and support for junior engineers.

Experience
- Bachelor’s or master’s degree in a relevant discipline or equivalent practical experience.
- Strong hands-on programming experience in Python or Java.
- Good working knowledge of SQL, including troubleshooting, optimisation and data analysis.
- Experience building or supporting production data pipelines in a collaborative engineering environment.
- Experience with distributed data processing frameworks such as Apache Spark.
- Familiarity with data formats such as JSON, Avro and Parquet.
- Understanding of temporal data modelling, schema design, partitioning and clustering techniques.
- Knowledge of software engineering fundamentals including version control, testing, release discipline and CI/CD practices.

About You
- Technically strong and pragmatic with a clear, structured approach to problem solving.
- Comfortable working in a fast-paced environment supporting important business outcomes.
- Willing to contribute to shared tooling or platform components to improve engineering effectiveness.
- Strong judgement in technical trade-offs and attention to detail in data correctness and testing.
- Ability to work closely with stakeholders and partner teams.
- Interest in developing long-term expertise within the firm.

Qualifications
- Relevant degree or equivalent practical experience demonstrating strong quantitative skills or data engineering expertise.
- Ability to learn new tools, internal platforms and delivery workflows quickly.
- For more experienced candidates: ability to lead delivery for a workstream, guide implementation standards, and support less experienced engineers.