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. In this data engineering role, you will design, build and maintain large-scale data platforms, deliver production pipelines, improve reliability and quality, and collaborate closely with platform users.

This delivery-focused role involves contributing to data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases. You may also help extend platform tooling where additional functionality is needed. You will work across ingestion, transformation, modelling, optimisation and data quality to deliver data products that are reliable, scalable and fit for purpose. The role suits engineers comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may contribute to technical design, platform standards and delivery approaches across a wider set of 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.
- Build reusable tooling to improve delivery, consistency and operational support.
- Ensure data pipelines are production-ready, well tested and operationally supportable.
- 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.
- Work with consumers to shape usable, well documented data products aligned to business needs.
- Implement controls to validate completeness, accuracy and consistency of data.
- Use reconciliation approaches to build confidence in production outputs and investigate issues.
- Contribute to standards for testing, monitoring and issue resolution.
- Collaborate closely with engineers, platform teams and data consumers to deliver outcomes on time and to quality standards.
- Communicate progress, risks, dependencies and design choices clearly.
- 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 common data formats such as JSON, Avro and Parquet.
- Understanding of temporal data modelling, schema design, schema evolution and data compatibility.
- Knowledge of partitioning, clustering and techniques to improve data performance at scale.
- Practical approach to data quality, reconciliation and root-cause analysis.
- Familiarity with software engineering fundamentals including version control, testing, release discipline and CI/CD practices.

About You
- Technically strong, pragmatic and comfortable working in a fast-paced environment.
- Takes ownership of the quality of solutions delivered in production.
- Strong judgement in technical trade-offs and attention to detail in data correctness and testing.
- Clear and structured approach to problem solving.
- Willingness to work closely with stakeholders and partner teams.
- Interest in contributing to shared tooling or platform components to improve the wider engineering environment.
- Ability to learn new tools, internal platforms and delivery workflows quickly.

Qualifications
- Bachelor’s or master’s degree in a relevant discipline or equivalent practical experience.

For More Experienced Candidates
- Stronger ownership of technical design across multiple datasets or pipeline domains.
- Experience guiding implementation standards, code quality and engineering practices within a team.
- Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.