Data, Lakehouse and AI Data Platform Engineer, Associate

Goldman Sachs

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Work type
On-site
Location
Dallas, TX
Posted
1 day ago

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How this pay compares to similar roles

Similar $189k
$148k most similar roles pay here $233k

This listing doesn't post a salary. Most similar roles pay $161,300–$217,600.

Based on 240 similar postings.

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About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 187 open roles on FindRole.

Listed pay typically runs $130,000–$250,000 across 60 roles with salary data.

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At a glance

TL;DR · Data, Lakehouse and AI Data Platform Engineer, Associate

As a Data Engineer in the Lakehouse and AI Data Platform team at Goldman Sachs, you will design, build, test, and support data pipelines and curated datasets on the firm’s modern data platform. Your responsibilities include developing robust data models, enhancing batch and streaming data pipelines using Python or Java, and ensuring data quality through validation and reconciliation processes. You will work with SQL, Apache Spark, Kafka, Snowflake, and other technologies to deliver scalable and reliable solutions that support analytics and operational decision-making. This role requires strong programming skills, familiarity with distributed data processing frameworks, and the ability to contribute to shared tooling for platform improvements. Ideal candidates are technically proficient, detail-oriented problem solvers who can work effectively in a fast-paced environment.

What you'll do

  • Design, build, and support batch and streaming data pipelines on the Lakehouse and AI data platform.
  • Develop raw, refined, and curated datasets to support analytics, reporting, and AI use cases.
  • Implement controls for validating completeness, accuracy, and consistency of data across pipelines.
  • Work with consumers to shape data products that are usable, well-documented, and aligned to business needs.
  • Contribute to shared tooling or framework components to improve platform functionality and reliability.

What we're looking for

  • Bachelor’s or master’s degree in a relevant discipline with strong quantitative skills or data engineering expertise.
  • Strong hands-on programming experience in Python or Java, and good working knowledge of SQL.
  • Experience building or supporting production data pipelines using distributed data processing frameworks like Apache Spark.
  • Understanding of temporal data modelling, schema design, partitioning, clustering, and other performance techniques at scale.
  • Familiarity with software engineering fundamentals including version control, testing, release discipline, and CI/CD practices.
  • Ability to work closely with stakeholders and partner teams, contributing to shared tooling or platform improvements.

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