Data Platform Engineer - Lakehouse & AI Pipelines

Goldman Sachs Group, Inc.; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Goldman Sachs Dallas is seeking a Data Engineer to join the Lakehouse and AI Data Platform. You will design, build, test and support data pipelines and datasets on the firm’s modern data platform, covering ingestion, transformation, modelling, and data quality.

The role emphasizes production readiness, reliability and collaboration with users. Ideal candidates have strong coding skills in Python or Java, solid SQL, and experience with distributed processing like Apache Spark; familiarity with

Qualifications

• Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience with strong quantitative skills.
• Strong hands-on programming in Python or Java.
• Good working knowledge of SQL, including troubleshooting, optimization 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.

Responsibilities

• Build and maintain data pipelines and curated datasets on a modern data platform.
• Design, test and support data flows across ingestion, transformation and modelling.
• Refactor and modernise data pipelines to improve reliability and performance.
• Develop reusable tooling to improve delivery and operational support.
• Collaborate with engineers and data consumers to deliver high quality data products.

Skills

Python

Java

SQL

CI/CD

Apache Spark

Kafka

JSON

Avro

Parquet

Data modelling

Education

Bachelor's or master's degree in a relevant discipline

Tools

Snowflake

Databricks

Hadoop

Kubernetes

Git

Job description

Goldman Sachs Dallas is seeking a Data Engineer to join the Lakehouse and AI Data Platform. You will design, build, test and support data pipelines and datasets on the firm’s modern data platform, covering ingestion, transformation, modelling, and data quality.

The role emphasizes production readiness, reliability and collaboration with users. Ideal candidates have strong coding skills in Python or Java, solid SQL, and experience with distributed processing like Apache Spark; familiarity with