Goldman Sachs Data Engineer Interview Questions + Guide in 2024

Goldman Sachs is a prestigious global investment banking, securities, and investment management firm established in 1869. Headquartered in New York, Goldman Sachs operates worldwide, leveraging technology and innovation to maintain its industry leadership.

In this guide, we will provide insights, sample Goldman Sachs data engineer interview questions, and tips to help you navigate the challenging interview process successfully. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a Goldman Sachs data engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV is shortlisted, you will be contacted by a recruiter from Goldman Sachs. This initial call, lasting about 30 minutes, is designed to verify critical details about your experiences and skill levels. Behavioral questions may also be included to assess your organizational cultural fit.

Technical Virtual Interview

The next stage involves a technical virtual interview, often conducted through platforms like CoderPad. This round typically lasts 1 hour and includes coding challenges. Examples of coding questions include list/dictionary manipulations, finding the maximum value in a dictionary, and solving algorithm problems similar to those on Leetcode (easy to medium level).

Onsite Interview Rounds

You will be invited for an onsite interview if you pass the technical round. This session consists of multiple rounds, including:

• Technical Discussions: These focus on your past projects and involve scenario-based questions and system design problems.
• Live Coding: You may encounter additional coding questions and SQL challenges.
• ETL and Data Modeling Questions: You will be assessed on your knowledge of data pipelines, data architecture, and ETL processes.

After the onsite interview, the hiring manager or recruiter will provide feedback and discuss the next steps.

Typically, interviews at Goldman Sachs vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

1. Create a function rain_days to calculate the probability of rain on the nth day after today.

The probability that it will rain tomorrow depends on whether it rained today and yesterday. If it rained both days, there’s a 20% chance it will rain tomorrow. If it rained one of the days, there’s a 60% chance. If it rained neither day, there’s a 20% chance. Given it rained today and yesterday, calculate the probability it will rain on the nth day after today.

2. How would you provide rejected loan applicants with reasons for rejection without access to feature weights?

Suppose you have a binary classification model that determines loan eligibility. As a financial company, you must provide each rejected applicant with a reason for their rejection. Given that you don’t have access to the feature weights, how would you generate these reasons?

3. Are we overestimating or underestimating the actual population’s credit score using a fixed cutoff?

Assume you have a credit model with a calibrated score (e.g., 83% with an actual range of 81%-85%). If you use 83% as a cutoff for creditworthiness, are you overestimating or underestimating the actual credit scores of the population?

4. How would you design an ML system to extract, transform, and store data from Reddit and Bloomberg APIs?

As a machine learning engineer for a large bank, you need to design a system that extracts data from the Reddit API (finance and news-related subreddits) and Bloomberg API (daily stock prices). How would you transform and store this data for use by downstream modeling teams?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Goldman Sachs data engineer interview include:

• Understand Data Systems: Familiarize yourself with large-scale data systems, ETL pipelines, and data architecture. Expect questions on Hadoop, Spark, and cloud-based data solutions.
• Prepare for Behavioral Questions: Be ready to discuss your past experiences, particularly focusing on collaboration, problem-solving, and dealing with challenging scenarios.
• Stay Calm and Structured: Ensure your answers are well-structured and you articulate your thought process clearly, even under pressure.

What is the average salary for a Data Engineer at Goldman Sachs?

What kind of technical skills are essential for a Data Engineer role at Goldman Sachs?

You should be proficient in multiple programming languages including Python, Java, and Scala. Experience with distributed data technologies like Hadoop, Spark, and MapReduce is crucial. You should also have a strong understanding of data architecture, modeling, and building workflows (ETL pipelines). Experience with cloud technologies such as AWS and data visualization tools such as Tableau is a plus.

What is the team environment like at Goldman Sachs for Data Engineering?

The team environment is highly collaborative and dynamic. Engineers work closely with product managers, user experience designers, and businesses to deliver cutting-edge data solutions. The role involves acting as a bridge between business processes and technology, enabling creativity and innovation while solving complex engineering problems.

Why is Goldman Sachs an attractive place to work for Data Engineers?

Goldman Sachs is a leading global investment banking, securities and investment management firm that commits its people, capital, and ideas to help clients, shareholders, and communities grow. You’ll have the opportunity to work on impactful projects in a fast-paced environment, utilizing cutting-edge technology. The firm values diversity, inclusion, and innovation, offering extensive training and development opportunities.

Interviewing for a Data Engineer position at Goldman Sachs involves a rigorous and multi-faceted process, encompassing a variety of coding, technical, and behavioral assessments. Candidates may face challenges such as coding on platforms like CoderPad, live coding rounds, as well as technical discussions focusing on data engineering principles, ETL pipelines, and system design. The experience can be intense due to the high standards and expectations set by the firm, but it offers a chance to demonstrate extensive technical skills and problem-solving abilities.

If you want more insights about the company, check out our main Goldman Sachs Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Goldman Sach’s interview process for different positions.

Good luck with your interview!