Software Engineering

Goldman Sachs Bank AG; Greater London; Hybrid; GBP 85,000 - 110,000; Full time

Benefits offered by this job

Healthcare coverage

Retirement benefits

Wellbeing programs

Job summary

Goldman Sachs is hiring a Data Engineer to design, build and operate data pipelines on a modern Lakehouse and AI data platform in London. You will contribute across ingestion, transformation, modelling and data quality to deliver reliable data products for analytics and AI use cases.

The role suits engineers who code in Python or Java, work with SQL, and collaborate with cross-functional teams to deliver robust data solutions. Candidates with Spark and Snowflake experience will thrive.

Qualifications

• Bachelor's or master's degree in a relevant field or equivalent practical experience.
• Strong programming in Python or Java with data engineering expertise.
• Good knowledge of SQL, troubleshooting and data analysis.
• Familiarity with CI/CD practices and version control.

Responsibilities

• Build, test and support data pipelines on a Lakehouse and AI data platform.
• Refactor data flows to improve reliability and performance.
• Create reusable tooling to improve delivery and operational support.
• Ensure pipelines are production-ready and well tested.

Skills

Python

Java

SQL

Apache Spark

CI/CD

Education

Bachelor's or Master's degree

Tools

Snowflake

Databricks

Apache Iceberg

Kafka

Parquet

JSON

Avro

Hadoop

Job description

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London location_on London, Greater London, England, United Kingdom

The Opportunity

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.

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.

The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of 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 where needed to improve reliability, performance and maintainability.
• Where needed, 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 these strengthen 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.

Skills and Experience

Required

• 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 PythonorJava.
• 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 the 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,AvroandParquet.

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 include:

• Data processing and logic:ANSI SQL, Apache Spark, Kafka
• 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

You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.

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

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

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.

Healthcare & Medical Insurance

We offer a wide range of health and welfare programs that vary depending on office location. These generally include medical, dental, short‑term disability, long‑term disability, life, accidental death, labor accident and business travel accident insurance.

Financial Wellness & Retirement

We assist employees in saving and planning for retirement, offer financial support for higher education, and provide a number of benefits to help employees prepare for the unexpected. We offer live financial education and content on a variety of topics to address the spectrum of employees’ priorities.

Health Services

We offer a medical advocacy service for employees and family members facing critical health situations, and counseling and referral services through the Employee Assistance Program (EAP). We provide Global Medical, Security and Travel Assistance and a Workplace Ergonomics Program. We also offer state‑of‑the‑art on‑site health centers in certain offices.

Fitness

To encouraging employees to live a healthy and active lifestyle, some of our offices feature on‑site fitness centers. For eligible employees we typically reimburse fees paid for a fitness club membership or activity (up to a pre‑approved amount).

Child Care & Family Care

We offer on‑site child care centers that provide full‑time and emergency back‑up care, as well as mother and baby rooms and homework rooms. In every office, we provide advice and counseling services, expectant parent resources and transitional programs for parents returning from parental leave. Adoption, surrogacy, egg donation and egg retrieval stipends are also available.

Benefits at Goldman Sachs

Read more about the full suite of class‑leading benefits our firm has to offer.