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$44.5K

$129.7K

$177.5K

How much do data engineer goldman sachs jobs pay per year?

As of Jul 13, 2026, the average yearly pay for data engineer goldman sachs in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

Can I make 200K as a data engineer?

Data engineers at Goldman Sachs can potentially earn 200K or more annually, especially with seniority, specialized skills in tools like Spark or Hadoop, and relevant certifications. Compensation varies based on experience, location, and performance, with higher salaries typically found in major financial hubs and for those with advanced expertise.

What is the salary of data engineer in Goldman Sachs?

The salary of a data engineer at Goldman Sachs typically ranges from $100,000 to $150,000 annually, depending on experience, location, and skill set. Entry-level positions may start lower, while experienced engineers with specialized skills in big data tools and cloud platforms can earn higher compensation.

What types of projects or challenges do Data Engineers typically work on at Goldman Sachs?

As a Data Engineer at Goldman Sachs, you can expect to work on projects involving the design, development, and optimization of large-scale data pipelines that support trading, risk analysis, and regulatory reporting. Challenges often include integrating complex data from multiple sources, ensuring data quality and consistency, and maintaining high performance and security standards. You will collaborate closely with data scientists, software engineers, and business analysts to enable data-driven decision making across various business units. This dynamic environment provides ongoing opportunities to implement innovative solutions and learn from experienced technical professionals.

What engineers make $500,000?

Senior data engineers, especially those with extensive experience, advanced skills in cloud platforms, and expertise in big data tools, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, particularly in large financial firms or tech companies with competitive pay structures.

What is a Data Engineer Goldman Sachs job?

A Data Engineer at Goldman Sachs designs, builds, and maintains scalable data pipelines and infrastructure to support analytics and decision-making. They work with large datasets, optimize data workflows, and ensure data quality and security. Using tools like SQL, Python, Spark, and cloud platforms, they collaborate with data scientists and business teams to drive insights. The role requires strong problem-solving skills, technical expertise, and knowledge of financial data.

What are the key skills and qualifications needed to thrive in the Data Engineer Goldman Sachs position, and why are they important?

To thrive as a Data Engineer at Goldman Sachs, you need strong expertise in data modeling, ETL processes, and programming languages such as Python, Java, or Scala, often backed by a degree in computer science or a related field. Familiarity with big data technologies (like Hadoop, Spark), SQL/NoSQL databases, and cloud platforms (e.g., AWS, Google Cloud) is also highly valued, with certifications being a plus. Excellent problem-solving abilities, attention to detail, and the capacity to work collaboratively in a fast-paced, globally distributed team set top candidates apart. These skills are crucial for building robust and scalable data infrastructure that supports high-impact decision making in a complex financial environment.

What is the Goldman Sachs 15 minute rule?

The Goldman Sachs 15 minute rule is a time management guideline encouraging employees, including data engineers, to respond to emails, messages, or tasks within 15 minutes to maintain productivity and effective communication. It promotes prompt action on urgent matters and helps prevent backlog in fast-paced financial environments.
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Infographic showing various Data Engineer Goldman Sachs job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.
Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas

Asset&Wealth Management-Senior Cloud Data Engineer-Vice President-Dallas

Goldman Sachs

Dallas, TX • On-site

$113K - $136K/yr

Other

Re-posted 14 days ago

Goldman Sachs rating

8.2

Company rating: 8.2 out of 10

Based on 26 frontline employees who took The Breakroom Quiz

44th of 149 rated banks

Job description

WM Data Engineering - Senior Cloud Data Engineer - Vice President 

Who We Look For: 

Goldman Sachs Engineers are innovators and problem-solvers, building solutions for various divisions. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.

We are seeking a high-caliber, hands-on Senior Cloud Data Engineer. While you will provide architectural guidance, your primary impact will come from hands-on engineering: building production-ready data pipelines, containerizing microservices for Amazon ECS, and executing the technical migration of legacy on-premises systems to AWS.

Key Responsibilities: 

  1. Hands-on Pipeline & Microservices Migration: 
    • Active Migration Execution: Directly execute the migration of legacy ETL and microservices to AWS. This includes refactoring monolithic code into containerized services and deploying them to Amazon ECS (Fargate/EC2).
    • Containerization & Orchestration: Build and maintain Docker images, write complex ECS Task Definitions, and configure service-to-service communication using Amazon ECS Service Connect and AWS Cloud Map.
    • Data Pipeline Engineering: Develop end-to-end data flows using AWS Glue (PySpark), Amazon EMR, and Snowflake. Implement "Lakehouse" patterns using Apache Iceberg to ensure data portability.
  2. Infrastructure & Automation-as-Code
    • IaC Development: Write and maintain production-grade Terraform or AWS CDK modules to provision VPCs, ECS clusters, and RDS instances. Ensure all infrastructure is version-controlled and deployed via GitHub Actions or GitLab CI.
    • AI-Augmented Coding: Actively use AI coding assistants (e.g., GitHub Copilot) to refactor legacy SQL, generate unit tests, and automate the creation of boilerplate pipeline code.
    • Toil Reduction: Identify manual bottlenecks in the migration process and build custom automation tools in Python or Go to streamline data validation and schema conversion.
  3. Technical Leadership & Reliability
    • Code Reviews & Standards: Lead rigorous peer code reviews, enforcing standards for performance, security (IAM least privilege), and maintainability.
    • Observability Implementation: Hands-on configuration of Amazon CloudWatch Container Insights, and OpenTelemetry to ensure deep visibility into migrated microservices and data jobs.
    • Performance Tuning: Directly optimize Spark job configurations, Snowflake warehouse sizing, and ECS auto-scaling policies to balance performance.

Qualifications: 

Technical Requirements

  • Experience: 8+ years of hands-on experience in Data Engineering and Cloud Infrastructure, with a focus on building and migrating production workloads.
  • AWS ECS Expertise: Deep technical expertise in Amazon ECS (Fargate/EC2), including networking (ALB/NLB), task placement strategies, and container security.
  • Data Platform Expertise: Proven experience with modern data platforms such as Snowflake (AI Data Cloud) and cloud-native services. Good understanding of open-source table formats, specifically Apache Iceberg, to enable interoperability, schema evolution, and high-performance analytics across multiple engines.
  • Programming: Expert-level proficiency in Java, Python and SQL. 
  • Big Data & Orchestration: Hands-on experience with Spark, Kafka, and orchestration tools like Apache Airflow, Dagster, or dbt.
  • Data Modeling: Deep understanding of data warehousing and modern data lakehouse architecture.

Leadership & Soft Skills

  • Mentorship: Proven track record of upskilling junior engineers.
  • Communication: Ability to explain complex technical concepts to non-technical stakeholders in the wealth management business.
  • Problem Solving: A "builder" mindset with the ability to navigate ambiguity in a fast-paced environment.

Education

  • Bachelor's or Master's degree in computer science, Engineering, Mathematics, or a related field.
ABOUT GOLDMAN SACHS

 
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. 

 
We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers. 

 
We're committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

 

 
The Goldman Sachs Group, Inc., 2023. All rights reserved.
Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

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About Goldman Sachs

Sourced by ZipRecruiter

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. We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

New York, NY, US

Year founded

1869

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