Goldman Sachs Software Engineer Interview Questions & Process

Preparing for a Goldman Sachs software engineer interview means stepping into an environment where high-performance engineering directly supports global markets, real-time risk evaluation, and rapidly modernizing financial platforms. As Goldman Sachs accelerates its shift toward cloud-native architecture, AI-driven risk modeling, and digital trading infrastructure, software engineers play a central role in building systems that must be both exceptionally reliable and fast.

In this guide, you will learn how the Goldman Sachs software engineer interview is structured across every stage, the technical and behavioral questions to expect, and examples that show how strong candidates approach their answers. This guide also includes targeted preparation strategies and a clear salary breakdown to support your offer planning.

Software engineers on Goldman Sachs engineering teams build the platforms that support global markets, digital banking, and real time risk systems. In a typical GS engineering role, you design high availability services that drive critical workflows across trading, risk, and internal operations.

The engineering culture emphasizes reliability, scalability, and thoughtful design choices, as reflected in the firm’s own description of its engineering organization. You can see examples in postings for Core Engineering roles and Platform Solutions engineering positions. Typical responsibilities include:

• Designing and maintaining distributed systems that power trading, clearing, and settlement workflows, similar to the requirements outlined in Goldman Sachs Global Markets engineering roles
• Building real time data pipelines that feed analytics and risk engines
• Developing internal platforms for identity management, ledgers, and client reporting
• Writing high performance Java or Python services that prioritize reliability and clear operational ownership
• Implementing observability and automation practices aligned with SRE principles
• Using containerization, Kubernetes, and event driven systems such as Kafka to support modernized infrastructure

This role is strategically important because the firm is investing heavily in cloud migration, unified data platforms, and scalable microservices, a direction emphasized throughout its engineering overview.

The Goldman Sachs interview process evaluates your technical depth, ability to reason about systems in real time, and clarity of communication. While the flow resembles processes at other top tech companies, GS places stronger emphasis on correctness, problem solving discipline, and business awareness. Candidate reports show a consistent pattern that includes an online assessment, a technical screen, and a Superday with multiple back to back interviews.

1. Application and resume screen

Recruiters scan for strong foundations in Java or Python, data structures, algorithms, and distributed systems. Candidates who highlight impact metrics, system ownership, or backend experience are more likely to advance. Recent interview reports mention that GS values resumes showing familiarity with real time systems or mission critical services. Recruiters also look for solid academic or project experience in computer science fundamentals, plus exposure to cloud or microservices environments.

Tip: Quantify improvements in speed, efficiency, or reliability on your resume to mirror expectations commonly used in ATS reviews.

2. Recruiter conversation

The recruiter conversation typically lasts 10 to 20 minutes and confirms your background, preferred teams, and readiness for the next stages. Candidates reported that this call is usually friendly and logistical, not a technical deep dive. You may be asked about the types of systems you have built, your familiarity with financial or distributed platforms, and why you are interested in Goldman Sachs engineering teams. Recruiters also outline the full process, including the online assessment and Superday timeline.

Tip: Prepare a concise story about mission critical systems you have supported and how your skills translate to GS engineering work.

3. Technical phone screens

Technical screens are consistently described as coding focused, often held on CoderPad or HackerRank. Candidates reported solving one or two LeetCode style problems in each session. Common patterns include monotonic stacks, hash maps, linked list manipulations, sliding windows, and classical questions like finding the next greater element or searching in a sorted array. Some interviews included database questions, object oriented concepts, or Java specific knowledge.

Online assessment details mentioned by candidates include:

• Two coding questions in 75 to 120 minutes
• Easy to medium difficulty
• DSA heavy focus on arrays, stacks, hash maps, and string problems

Tip: Practice coding in a plain editor and speak through your logic clearly. GS interviewers frequently ask for reasoning, alternative approaches, and complexity analysis.

4. Onsite interview loop

The onsite or Superday is the core of the GS coding interview experience. Candidates report three to four back to back sessions, each about 30 to 45 minutes. These generally include:

• Coding and algorithms: Variants of LeetCode problems, often easy to medium difficulty. Examples include car fleet, hash map implementations, longest repeating substring, and container with most water.
• System design: Lightweight system design such as designing a backend for a flash sale, SQL versus NoSQL decisions, or explaining how to build small scale distributed components.
• Technical fundamentals: Questions on data structures, hash collisions, linked lists, overriding and overloading, or object oriented design.
• Behavioral fit: STAR style interviews focusing on teamwork, decision making, and deep dives into your projects. Some rounds are almost entirely behavioral with many follow ups about technical decisions.

Candidate feedback shows that interview quality varies by team. Some experiences describe supportive interviewers and clear expectations, while others note slow communication or inconsistent question difficulty.

Tip: Start each system design discussion by naming constraints first such as latency, throughput, storage behavior, and failure handling.

5. Hiring committee and offer

When the Superday concludes, your performance is reviewed holistically by a hiring group. Feedback includes your coding accuracy, system thinking, communication, and alignment with GS engineering culture. The decision timeline ranges from a few days to several weeks based on team load. If approved, you move into offer discussions and potential team matching.

Tip: Use Levels.fyi benchmarks to prepare for compensation discussions and ensure you understand ranges for analyst, associate, and senior engineering levels.

Data Structures & Algorithms Interview Questions

Goldman Sachs emphasizes strong fundamentals in data structures and algorithms because its systems must remain correct, efficient, and predictable under real time load. These questions reveal how well you translate ambiguous problem statements into clean, scalable solutions that handle edge cases and tight runtime constraints.

1. Design an O(n) algorithm to compute trapped rainwater over a 1D terrain profile

   This problem checks whether you can turn a real world description of “bars and water” into a linear time, linear space array algorithm using prefix and suffix maxima or a two pointer sweep. At Goldman Sachs, software engineers get questions like this to show they can build efficient, edge case aware solutions that still perform well under tight runtime constraints.

   Example:

   Index	0	1	2	3	4
   Terrain Level	3	0	2	0	4
   Trapped Water	0	3	1	3	0

2. Merge two sorted lists in linear time and analyze the complexity

   Here the interviewer wants to see if you can apply the two pointer pattern to merge two already sorted sequences into a single ordered list in O(n₁ + n₂) time. At Goldman Sachs, this maps directly to tasks like merging sorted logs or datasets, so they use it to verify that you understand both correctness and time or space tradeoffs at scale.

   Input:

   list1 = [1,2,5]
   list2 = [2,4,6]
   

   Output:

   def merge_list(list1,list2) -> [1,2,2,4,5,6]
   

3. Design an efficient algorithm to compute the longest increasing subsequence length

   This question evaluates whether you recognize the longest increasing subsequence structure and can choose between a dynamic programming solution and the more scalable binary search based approach. Goldman Sachs uses it to test your ability to pick and implement an algorithm that remains predictable and performant on large inputs such as those found in trading or risk systems.

4. Design and implement Dijkstra’s shortest-path algorithm on a weighted graph

   The focus here is on modeling a weighted graph correctly, then using Dijkstra’s algorithm with a priority queue to compute minimum cost paths. Interviewers at Goldman Sachs ask this to see if you can design routing or dependency logic that accounts for latency and cost while remaining both correct and efficient in large distributed environments.

5. Find the lowest common ancestor of two nodes in a binary tree

   This problem tests your ability to traverse a binary tree, reason recursively, and carefully handle cases where one or both target nodes might be missing. Goldman Sachs uses it to check that you are comfortable with pointer based data structures and hierarchical relationships, which show up in dependency graphs, entitlement trees, and configuration systems where correctness and traceability are critical.

   Input:

   # Diagram of the binary tree
   '''
         6
        / \
       3   9
      / \
     2   11
        / \
       5   8
   '''
   value1 = 8
   value2 = 2
   

   Output:

   common_ancestor(root,value1,value2) -> 3
   

6. Explain how you would sort a 100GB file with only 10GB of RAM

   This question is about external sorting, where you split a huge file into memory sized chunks, sort each chunk, then run a k way merge (often via a min heap) over the sorted runs on disk. Goldman Sachs interviewers ask this to see whether you can design scalable, disk aware systems and reason explicitly about constraints like RAM, I or O throughput, and buffer management in real production workloads.

System Design Interview Questions

System design questions at Goldman Sachs focus on building reliable, auditable, and low latency components that fit within highly regulated financial environments. Interviewers want to see whether you can reason from constraints, evaluate tradeoffs, and design systems that remain stable even when traffic volumes or compliance requirements surge.

1. Design a real-time transaction streaming architecture for fraud detection and reporting

   This problem checks whether you can move from batch ingestion to a real time streaming design that validates, enriches, and scores transactions as they arrive. Goldman Sachs uses it to evaluate if you can build fault tolerant, well governed systems that support fraud detection, risk monitoring, and regulatory reporting while preserving ordering, consistency, and auditability.

   

2. Design a centralized real-time event ingestion and processing pipeline for Goldman Sachs systems

   Here the interviewer is looking for a robust, low latency event ingestion layer that can safely collect, validate, route, and process high volume events from many internal producers. At Goldman Sachs, this kind of design must guarantee durability, replayability, and clear audit trails, so the question tests whether you understand enterprise grade requirements around consistency and resiliency.

3. Define functional vs non-functional requirements for a secure payment-API datastore at Goldman Sachs

   This question asks you to separate what a payment datastore must do functionally (store users, cards, transactions, balances) from how it must behave (secure, ACID compliant, scalable, and auditable). Goldman Sachs uses it to see if you think beyond schema design into topics like PCI, encryption, consistency guarantees for money movement, and regulatory expectations.

4. Design a System to Enforce Repository Policies in a Multi-Team Git Workflow

   The goal here is to design an automated enforcement layer around Git and CI or CD that prevents disallowed file types and risky code patterns from reaching protected branches. Interviewers at Goldman Sachs use this to test whether you can build secure, auditable guardrails that fit into developers’ workflows without adding unnecessary friction in a regulated environment.

5. Design a Secure, Compliant Internal Messaging Platform for Goldman Sachs

   This problem explores how you would design an internal messaging system that offers reliable delivery, cross device sync, strong encryption, and long term retention under compliance rules. Goldman Sachs asks it to assess whether you can balance low latency communication needs with strict requirements around security, supervision, and auditability.

   System Overview Diagram

   graph TD
       A[User Device] -->|Send/Receive Messages| B(Messaging Service)
       B -->|Persist Messages| C[Message Storage]
       B -->|Encrypt Messages| D[Encryption Module]
       B -->|Log User Activity| E[Audit Logging]
       C -->|Retrieve History| A
       E -->|Store Logs| F[Compliance Storage]
       B -->|Queue Messages| G[Message Queue]
       G -->|Deliver Messages| A
       B -->|Scale Services| H[Scalability Layer]
       H -->|Load Balance Traffic| I[Load Balancer]
   

SQL Interview Questions

SQL interviews evaluate how well you can transform raw transactional or time series data into accurate, production ready outputs. At Goldman Sachs, engineers frequently write analytical and regulatory queries, so interviewers look for precision, correct handling of ordering, and strong understanding of window functions.

1. Write a SQL Query to Derive Time-Based User Sessions from Event Logs

   This question evaluates your ability to implement sessionization in SQL using time gaps, window functions like LAG, and cumulative grouping to form coherent user sessions. At Goldman Sachs, similar logic is used on trading events, internal usage logs, and audit trails, so they want to see that you can turn a precise business definition of a session into an edge case safe query.

   Input:

   events table

   Column	Type
   id	INTEGER
   created_at	DATETIME
   user_id	INTEGER
   event	VARCHAR

   Output:

   Column	Type
   created_at	DATETIME
   user_id	INTEGER
   event	VARCHAR
   session_id	INTEGER

2. Write a Monthly Revenue and Customer Aggregation Query for Transaction Data

   Here the interviewer wants to know if you can combine joins, date extraction, and aggregations to produce a clean monthly report of unique customers, order counts, and revenue. Goldman Sachs uses this kind of problem to check that you can express reporting requirements as efficient SQL that could back dashboards, regulatory filings, or internal analytics on trades and payments.

3. Write a SQL Query to Retrieve the Final Transaction of Each Day

   This problem tests whether you can work correctly with time ordered data, using window functions or grouped aggregates to pull the last transaction per day while keeping timestamps and values aligned. At Goldman Sachs, engineers often analyze high volume time series from trading and risk systems, so this question checks your ability to extract the latest state reliably and avoid nondeterministic grouping errors.

   Input:

   bank_transactions table

   Column	Type
   id	INTEGER
   created_at	DATETIME
   transaction_value	FLOAT

   Output:

   Column	Type
   created_at	DATETIME
   transaction_value	FLOAT
   id	INTEGER

4. Calculate Cumulative Sales Since Last Restock with Window Functions

   This question examines whether you can combine joins and window functions to compute a running total that resets when a restock event occurs. Goldman Sachs uses analogous patterns for metrics like exposure since last margin call or orders since a limit reset, so they ask this to ensure you can encode business rules as precise SQL without off by one mistakes.

5. Compute Running Sales Since Last Restock for Each Product

   Here the interviewer is checking that you can express a resettable cumulative metric for each product by joining the right tables and partitioning window functions properly. At Goldman Sachs, similar queries track values since the last funding event, rebalance, or cutoff, so this tests your ability to write robust, production ready SQL over transactional data.

   Input:

   subscriptions table

   Column	Type
   user_id	INTEGER
   start_date	DATETIME
   end_date	DATETIME

   user_id	start_date	end_date
   1	2019-01-01	2019-01-31
   2	2019-01-15	2019-01-17
   3	2019-01-29	2019-02-04
   4	2019-02-05	2019-02-10

   Output:

   user_id	overlap
   1	1
   2	1
   3	1
   4	0

Behavioral Interview Questions

Behavioral rounds assess how you communicate, collaborate, and make decisions under pressure while working across teams like trading, risk, and compliance. Goldman Sachs expects thoughtful reflection on past work, clear explanation of technical choices, and an ability to stay structured and calm during ambiguity.

1. Walk Me Through a Data Project and Its Key Challenges

   This question focuses on how you plan, execute, and troubleshoot an end to end data or engineering project rather than just your coding skills in isolation. Goldman Sachs uses it to understand how you handle messy requirements, coordinate with stakeholders, and manage constraints like data quality, latency, and risk controls.

2. Tell me about your strengths and weaknesses

   Here the interviewer is assessing your self awareness, communication skills, and honesty about how you work. Goldman Sachs wants software engineers who can operate in high pressure, cross functional settings, so they look for strengths tied to engineering impact and weaknesses framed as active improvement efforts.

3. How comfortable are you presenting your insights?

   This question probes how well you can turn technical work into clear, actionable messages for nontechnical audiences. At Goldman Sachs, engineers frequently brief risk, compliance, trading desks, and leadership, so interviewers look for candidates who can explain assumptions, constraints, and tradeoffs with confidence and clarity.

4. Explain Why You Want to Work at Goldman Sachs and Why You’re a Strong Fit

   The interviewer is trying to understand your motivation, how well you grasp the firm’s engineering culture, and whether your goals line up with what the role actually demands. Strong answers at Goldman Sachs connect your experience to themes like ownership, rigor, long term impact, and collaborative work on critical systems.

5. Describe a Stakeholder Communication Breakdown and How You Resolved It

   This question explores how you respond when communication fails between teams and how you restore alignment. Goldman Sachs asks it because engineers regularly work with stakeholders who have different vocabularies and priorities, and they want to see that you can keep trust and clarity intact even when there is pressure or ambiguity.

Preparing for a Goldman Sachs software engineer interview requires a structured approach that mirrors how top candidates prepare for companies like Google, Amazon, Adobe, and Spotify. Effective GS interview prep focuses on technical fundamentals, targeted system design practice, behavioral clarity, and understanding the financial context in which GS engineering teams operate. Use the breakdown below to guide your preparation plan.

1. Strengthen your technical coding foundation

Most online assessments and technical screens require consistent Goldman Sachs coding prep. Candidate reports show a strong emphasis on medium difficulty LeetCode style problems that involve:

• Hash maps, stacks, and queues
• Monotonic stack patterns such as next greater element
• Sorting and two pointer techniques
• Graph and string manipulation problems
• Basic object oriented concepts and Java fundamentals

Treat each problem as a full reasoning exercise. Clarify constraints, walk through examples, write clean code, and analyze time and space complexity. This mirrors expectations in Google and Amazon style screens where communication and structure matter as much as correctness. You should also practice in a plain editor to build discipline and reduce reliance on autocomplete tools.

2. Build targeted system design skills

Goldman Sachs typically asks small to medium scale design questions. These differ from large architectural questions seen in senior FAANG loops. Expect scenarios such as:

• Designing a backend for a flash sale
• Choosing between SQL and NoSQL for a workload
• Building a basic event processing or pub or sub workflow
• Introducing caching, queuing, or rate limiting

As seen in Adobe and Amazon interviews, start with constraints and requirements. Identify bottlenecks, propose alternatives, and discuss tradeoffs related to latency, throughput, storage durability, and fault tolerance. Emphasize reliability and determinism because GS works heavily with trading and risk systems.

3. Build awareness of financial context

You do not need finance expertise, but you should understand why reliability, auditability, and predictable performance matter in environments that support trading and risk calculations. When answering design or coding questions, mention how your decisions protect data integrity and avoid cascading failures.

Goldman Sachs software engineers earn competitive compensation across levels, according to Levels.fyi. Across the United States, total annual compensation typically ranges from about $118K for Analyst roles to $232K or more for Vice Presidents, with a median around $150K depending on location and team. New York City consistently reports the highest pay bands, followed by markets such as Dallas and Los Angeles. Compensation varies based on seniority, business division, and whether an engineer supports trading, platform, or enterprise systems. RSUs remain a meaningful component of long term compensation and follow a three year vesting schedule with even annual distribution.

Compensation by level

Compensation typically rises significantly after equity vesting begins in year two.

Regional salary comparison

According to Levels.fyi, compensation differs across U.S. regions where Goldman Sachs maintains engineering teams. New York City shows the highest levels of total compensation, while Dallas and Los Angeles offer competitive pay with lower cost of living. These differences reflect market demand, team distribution, and the concentration of trading or platform engineering roles.

1. What is the average Goldman Sachs software engineer salary?

Goldman Sachs software engineers typically earn between $118K and $232K per year from Analyst to Vice President levels, according to Levels.fyi. Compensation varies by location and team, with New York City offering the highest ranges and equity becoming more meaningful after year two.

2. How hard is the Goldman Sachs software engineer interview?

The interview is moderately difficult and focused on strong fundamentals. Candidates can expect medium level LeetCode style coding problems, small scale system design, and behavioral deep dives centered on ownership and decision making.

3. Do I need finance experience to get hired as a Goldman Sachs software engineer?

No finance experience is required. What matters is your ability to design reliable systems, explain tradeoffs clearly, and understand why data integrity and predictable performance matter in trading and risk driven environments.

4. Which programming languages and technologies does Goldman Sachs prefer?

Most teams lean heavily on Java and Python, along with distributed systems concepts, Kubernetes, Kafka, and strong observability practices. Consistency in one primary language is valued during coding interviews.

5. What is the structure of the Goldman Sachs software engineer interview process?

Candidates typically go through a resume screen, recruiter call, technical screens via CoderPad or HackerRank, and a Superday with coding, system design, and behavioral interviews. A hiring committee reviews performance holistically before extending an offer.

6. How should I prepare for the GS coding and system design interviews?

For coding, practice medium difficulty DSA problems in a plain editor and explain your reasoning aloud. For system design, focus on constrained scenarios like flash sale backends or simple event pipelines and start with latency, throughput, and reliability requirements.

7. What does the hiring committee look for after the Superday?

The committee evaluates coding accuracy, system thinking, communication, and cultural fit across all interviews. Clear signals of ownership, disciplined problem solving, and strong collaboration patterns strengthen your chances.

Succeeding in the Goldman Sachs software engineer interview requires a balanced approach that strengthens your coding fundamentals, sharpens your system design skills, and prepares you to communicate clearly in high pressure, cross functional environments. By understanding how each stage works and practicing with realistic examples, you can demonstrate the technical depth and disciplined problem solving that Goldman Sachs looks for in its engineering hires.

To build the foundation expected in GS interviews, explore the Interview Query Data Structures and Algorithms Learning Path for guided practice and structured skill development. For a real example of successful preparation, read Nathan Fritter’s Interview Query success story and see how focused, consistent effort can transform your interview performance.