Overview
Join a team building the data foundations that support the firm’s AI and analytics capabilities. This role sits within 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.
Job Description
At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large‑scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.
This is a delivery‑focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision‑making and emerging AI use cases, and may also help extend platform tooling where additional functionality is needed.
Role Summary
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. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.
Key Responsibilities
Pipeline Engineering
- Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
- Refactor or modernise existing data flows where needed 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.
Data Modelling and Curation
- Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
- Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
- Work with consumers to shape data products that are usable, well documented and aligned to business needs.
Data Quality and Reconciliation
- Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
- Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
- Contribute to clear standards for testing, monitoring and issue resolution.
- Contribute to practical improvements in testing, monitoring or reconciliation tooling where this strengthens platform reliability and day‑to‑day delivery.
Delivery and Partnership
- Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
- Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
- For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.
Required Skills and Experience
- Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
- Strong hands‑on programming experience in Python or Java.
- Good working knowledge of SQL, including troubleshooting, optimisation and data analysis.
- Ability to learn new tools, internal platforms and delivery workflows quickly.
- Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.
Data Engineering Capability
- Understanding of temporal data modelling, including handling of historical state and change over time.
- Knowledge of schema design, schema evolution and data compatibility considerations.
- Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
- Ability to make sensible design choices across normalised and denormalised models, and between natural and surrogate keys.
- Practical approach to data quality, reconciliation and root‑cause analysis.
- Experience building or supporting production data pipelines in a collaborative engineering environment.
- Experience working with distributed data processing frameworks such as Apache Spark.
- Working knowledge of common data formats such as JSON, Avro and Parquet.
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.
Technology Environment
The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.
Examples of Technologies In Scope
- Data processing and logic: ANSI SQL, Apache Spark, Kafka
- Data formats: JSON, Avro, Parquet
- Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
- Engineering and deployment: CI/CD tooling, containerised or Kubernetes‑based deployment approaches where relevant
What We Are Looking For
We are looking for engineers who can deliver well‑structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast‑paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.
Stronger Candidates Will Typically Demonstrate
- Sound judgement in technical trade‑offs
- Attention to detail in data correctness and testing
- A clear and structured approach to problem solving
- Willingness to work closely with stakeholders and partner teams
- An ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
- An interest in developing long‑term expertise within the firm
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.
We are 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
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