Software Engineering - Data, Lakehouse and AI Data Platform Engineer - Vice President - Dallas

Goldman Sachs

Dallas (TX)

On-site

USD 140,000 - 190,000

Full time

29 hours ago

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Job summary

Goldman Sachs is recruiting a Data Engineer to design, build and operate data pipelines on a modern Lakehouse and AI data platform. You will work across ingestion, transformation, modelling, optimization and data quality to deliver scalable data products for analytics and AI workloads.

Ideal candidates have 7–12+ years in data engineering, strong Python/Java skills and deep SQL expertise. The role emphasizes production-grade pipelines, tooling, and collaboration with platform teams to support

Qualifications

7–12+ years of experience in data engineering or related field.
Strong programming experience in Python or Java.
Proficient SQL skills and data analysis.
Ability to design and optimize batch and streaming data pipelines.
Experience with distributed processing frameworks and data formats.

Responsibilities

Design, build, test and support data pipelines on a modern Lakehouse and AI platform.
Develop raw, refined and curated datasets for analytics and AI use cases.
Ensure data quality, reconciliation and testing across pipelines.
Collaborate with engineering teams and data consumers to deliver data products on time.

Skills

Python

Java

SQL

CI/CD

Data architecture

Problem solving

Education

Bachelor's or Master's in CS/Data/Engineering

Tools

Apache Spark

Kafka

Snowflake

Databricks

Hadoop ecosystem

JSON/Parquet

Job description

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.

Job Description

The Opportunity

At Goldman Sachs, engineering teams are positioned at the center 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, optimization 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

Pipeline Engineering

Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
Refactor or modernize 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.

Required

Skills and Experience

7-12+ years of experience
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, 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.

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 normalized and deformalized 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.
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
Data formats: JSON, Avro, Parquet
Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
Engineering and deployment: CI/CD tooling, containerized 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