What you will do

Data Engineer - Lakehouse and AI Data Platform - VP - Warsaw

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.

Role Summary

As a Data Engineer, Lakehouse and AI Data Platform, 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.

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

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.

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.

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.

Take a broader role in technical leadership, task breakdown and support for junior engineers.

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.

What we offer

We Offer Best-In-Class Benefits

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.

Holiday & Vacation Policies

We offer competitive vacation policies based on employee level and office location. We promote time off from work to recharge by providing generous vacation entitlements and a minimum of three weeks expected vacation usage each year.

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 encourage 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.
Learn More

Requirements

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 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

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.

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, Avro and Parquet.

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

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

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.

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 interest in developing long-term expertise within the firm

Data Engineer – Lakehouse and AI Data Platform – Vice President