Data Platform Engineer – Lakehouse & AI Pipelines

Socket.dev; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Job summary

Goldman Sachs is seeking a Data Engineer to design, build, test and support data pipelines on a modern Lakehouse and AI data platform. You will work across ingestion, transformation, modelling, optimisation and data quality to deliver reliable, scalable data products.

Ideal candidates have strong Python or Java skills, solid SQL knowledge and experience with distributed data processing such as Apache Spark.

Qualifications

• 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.
• Good working knowledge of SQL, including troubleshooting, optimization and data analysis.
• Ability to learn new tools, internal platforms and delivery workflows quickly.
• Familiarity with version control, testing, release discipline and CI/CD practices.

Responsibilities

• Build, test and support data pipelines on the Lakehouse and AI data platform.
• Develop raw, refined and curated datasets for analytics and AI use cases.
• Implement data quality controls and reconciliation to ensure production reliability.
• Collaborate with engineers and data consumers to deliver outcomes on time and with quality.
• Contribute to shared tooling and platform components to improve the engineering environment.

Skills

Python

Java

SQL

CI/CD

Version control

Testing

Distributed data processing

Education

Bachelor's or Master's in STEM

Tools

Apache Spark

Kafka

Job description

Goldman Sachs is seeking a Data Engineer to design, build, test and support data pipelines on a modern Lakehouse and AI data platform. You will work across ingestion, transformation, modelling, optimisation and data quality to deliver reliable, scalable data products.

Ideal candidates have strong Python or Java skills, solid SQL knowledge and experience with distributed data processing such as Apache Spark.