Goldman Sachs Engineering Analyst 2025: The 10.5-Hour Campus Marathon, 5 of 24 Selected

TL;DR. ECE student, 2026 batch, with startup experience building a quantitative trading firm. Goldman Sachs on-campus recruitment, August 25, 2025. Entered the VH3 building at 12:00 PM, walked out at 10:30 PM. Four rounds across 10.5 hours: HackerRank technical test (OS, DBMS, Networks MCQs plus medium-level coding), system design with DSA (Asteroid Collision, Trapping Rain Water), advanced algorithms with UML diagrams (KMP, Rabin-Karp for Repeated String Match), and a VP engineering manager round on quantitative trading strategies and compliance. Result: selected. 5 out of 24 shortlisted candidates made the cut.

This is not a "Goldman Sachs recruitment process overview." Those exist on every placement prep site and they tell you the same thing: aptitude, coding, interview. This is one specific candidate's specific August 2025 on-campus loop, with the actual questions they were asked, the actual algorithms they had to implement, and the actual VP conversation that closed the day.

The verified candidate

The candidate, Debjyoti Ray, was not a standard CS student walking into a Goldman Sachs drive with LeetCode premium and Striver. He came from ECE with a genuine quantitative trading background, a startup where he had built trading systems, and a working knowledge of finance that most engineering analysts spend their first year acquiring on the job. This mattered in Round 4.

The campus drive structure

Goldman Sachs on-campus drives follow a tournament bracket. The initial HackerRank test screens the full eligible pool down to a shortlist (24 in this cycle). Those 24 then go through three back-to-back interview rounds in the same day at the same venue. You arrive in the morning, and you leave at night. There is no "we will schedule your next round next week." The entire assessment pipeline collapses into a single day.

This matters for preparation. You are not optimizing for one 45-minute coding round. You are optimizing for sustained performance across 10 or more hours, with increasing difficulty and different interviewers, on the same day. Physical stamina, food timing, and mental reset between rounds are not soft factors. They are engineering constraints.

Round 1: HackerRank Technical Test (1 hour 30 minutes)

The HackerRank test is the filter from the broader campus pool to the 24-person shortlist. Three sections, no breaks between them, 90 minutes total.

The CS MCQs covered Operating Systems (process scheduling, deadlocks, memory management), DBMS (normalization, transactions, indexing), and Computer Networks (TCP/IP, OSI model, routing protocols). Fifteen questions in 30 minutes means 2 minutes per question with no room for deliberation. If you do not have core CS memorized to recall speed, the MCQ section burns time that you need for the coding section.

The two programming problems were medium-level. Exact problem statements are not disclosed, but the difficulty calibration at Goldman Sachs on-campus drives typically falls in the range of sliding window, two-pointer, or basic graph traversal. Not LeetCode hard, but not FizzBuzz either.

The subjective section, 2 behavioral questions in 15 minutes, is the section most candidates ignore during prep. Goldman Sachs weights written communication more heavily than most tech companies. A "tell me about a time" answer written in broken, unstructured English will cost you shortlist points even if your coding was clean.

Prep implication. The MCQ section is the silent killer. Most candidates prep only DSA. Goldman's HackerRank test checks core CS fundamentals at speed. Spend two focused days on OS (Galvin chapters 5-8), DBMS (Navathe normalization and transaction chapters), and CN (Kurose chapters 3-4) before any GS drive.

Round 2: System Design + DSA (1 hour)

The first interviewer was an Associate, roughly equivalent to SDE-2 at a product company. The round opened with a discussion of the candidate's own AWS project, which transitioned into a low-level design exploration. This is the "tell me about something you built, and now let me stress-test your design decisions" format. If you list a project on your resume, you must be able to defend its architecture under adversarial questioning.

The DSA portion asked two named problems.

Asteroid Collision (LeetCode 735). Given an array of asteroids where positive values move right and negative values move left, determine which asteroids survive after all collisions. The canonical solution uses a stack. You iterate through the array, pushing positive asteroids onto the stack. When you encounter a negative asteroid, you compare its absolute value against the top of the stack and resolve collisions. Key edge cases: equal-size collision (both destroyed), the negative asteroid being larger than multiple stack elements (pop multiple times).

Trapping Rain Water (LeetCode 42). Given an elevation map, compute how much water it can trap after raining. The candidate was expected to start with the DP approach (precompute left-max and right-max arrays, O(n) time, O(n) space) and then optimize to the two-pointer approach (O(n) time, O(1) space). Goldman interviewers at the Associate level expect you to demonstrate the optimization path, not just land on the optimal solution directly. Showing you can start brute-force and refine is the signal.

Round 3: Advanced Algorithms + OOPs (1 hour 20 minutes)

The second interview round was the hardest. The interviewer was a senior Associate, equivalent to SDE-3, and the round lasted 80 minutes instead of the standard 60.

The LLD portion required the candidate to draw UML diagrams, not pseudocode, not a whiteboard sketch of boxes. Actual UML class diagrams with inheritance arrows, composition diamonds, and method signatures. Most candidates have never drawn a formal UML diagram in their life. If you are interviewing at Goldman Sachs, practice UML notation for 3 to 4 standard designs: observer pattern, factory pattern, strategy pattern, and decorator pattern.

The OOPs segment went deeper than "explain polymorphism." The interviewer probed edge cases: when does method overriding fail to provide true polymorphism? What breaks encapsulation in practice? When is abstraction a liability rather than an asset? These are questions that separate candidates who memorized definitions from candidates who have built real systems.

Repeated String Match (LeetCode 686). Given two strings A and B, find the minimum number of times A must be repeated such that B is a substring of the repeated string. The brute-force approach concatenates A repeatedly and checks for substring containment. The interviewer expected the candidate to implement both KMP (Knuth-Morris-Pratt) and Rabin-Karp string matching algorithms and discuss the tradeoffs.

KMP builds a failure function (prefix table) in O(m) time, then matches in O(n) time, giving O(n+m) total. Rabin-Karp uses a rolling hash to check substring matches in expected O(n+m) time, but worst case O(nm) due to hash collisions. The tradeoff: KMP has guaranteed linear time but is harder to implement correctly under pressure. Rabin-Karp is simpler to code but its worst case is quadratic. Goldman interviewers want you to articulate this tradeoff explicitly, not just implement one.

This is the round that eliminates most candidates. The combination of formal UML, deep OOPs, and advanced string algorithms in a single 80-minute round is calibrated to find candidates who have depth across multiple areas simultaneously. Studying DSA alone will not clear this round. Studying OOPs alone will not clear this round. Goldman's on-campus loop demands breadth at depth.

Round 4: Engineering Manager (35 minutes)

The final round was the shortest but the most decisive. The interviewer was a Vice President, and the conversation was not technical in the DSA sense. It was technical in the finance sense.

The VP asked about quantitative trading portfolio strategies. This is where the candidate's startup background paid off. Having actually built a trading firm, the candidate could discuss portfolio construction, risk management, hedging strategies, and execution optimization from first-hand experience. Most engineering analyst candidates walk into this round with zero finance vocabulary and try to wing it.

The compliance discussion probed regulatory awareness: what constraints exist on algorithmic trading, what data handling requirements apply to financial institutions, what happens when an engineering decision conflicts with a regulatory requirement. Goldman Sachs is not a tech company that happens to trade. It is a financial institution where technology serves regulation. Candidates who understand this distinction advance. Candidates who treat it as "just another tech company" do not.

Prep implication for the EM round. If you are interviewing for Goldman Sachs Engineering Analyst from an ECE or CS background with no finance exposure, spend one weekend reading: (1) the Wikipedia pages for market microstructure, algorithmic trading, and portfolio management, (2) Goldman's annual report engineering section, and (3) basic SEBI regulations on algorithmic trading in India. You do not need a CFA. You need enough vocabulary to not blank when a VP asks "how would you handle a scenario where your model's output conflicts with regulatory position limits?"

The numbers that matter

Real questions asked in this loop

1. Asteroid Collision (stack): resolve directional collision of positive/negative integers in an array.
2. Trapping Rain Water (DP to two-pointer): compute trapped water on an elevation map, demonstrate optimization from O(n) space to O(1).
3. Repeated String Match (KMP, Rabin-Karp): minimum repetitions of string A for B to be a substring, implement both string-matching algorithms.
4. LLD of AWS project: defend architecture decisions from candidate's own experience.
5. UML class diagrams: formal notation, not whiteboard sketches.
6. OOPs edge cases: when does polymorphism break, when is abstraction a liability.
7. Quantitative trading strategies: portfolio construction, risk management, hedging, execution optimization.
8. Compliance scenarios: regulatory constraints on algorithmic trading, data handling requirements, engineering vs. regulatory conflicts.

How to survive a 10-hour campus drive

1. Core CS fundamentals are tested at speed. The MCQ section (OS, DBMS, CN) eliminates candidates before the coding even starts. Two days of textbook revision (Galvin, Navathe, Kurose) is not optional.
2. Learn formal UML notation. Goldman's LLD rounds use UML diagrams, not pseudocode boxes. Practice drawing class diagrams with proper notation for 4 to 5 standard design patterns.
3. String algorithms beyond the basics. KMP and Rabin-Karp are not commonly tested at most product companies. Goldman expects them. Practice both implementations until you can write them from scratch in under 10 minutes.
4. Finance vocabulary is not optional. Even for Engineering Analyst roles, the VP round probes financial understanding. One weekend of reading (market microstructure, algorithmic trading basics, SEBI regulations) separates you from candidates who blank on finance questions.
5. Stamina is an engineering constraint. 10.5 hours in one building, four rounds, increasing difficulty. Eat before Round 1, carry snacks, practice resetting focus between mock rounds during prep. The candidate who codes well at hour 2 but crumbles at hour 8 does not get the offer.

Source and verification

This analysis is based on a published interview experience on Medium via the Nybles publication. The candidate, Debjyoti Ray, disclosed their name, ECE branch, 2026 batch, quantitative trading background, and the specific date (August 25, 2025). All four rounds are described with named problems (Asteroid Collision, Trapping Rain Water, Repeated String Match), interviewer levels (Associate, Senior Associate, VP), and the selection outcome (5 of 24).

Original article: Goldman Sachs Engineering Analyst Interview Experience (On Campus)

PapersAdda verification standard: publicly accessible URL, per-round detail, named problems, and explicit outcome. This source meets all four criteria.

What campus candidates ask about Goldman Sachs

What is the Goldman Sachs Engineering Analyst role? Engineering Analyst is Goldman's entry-level engineering position for campus hires. It maps roughly to SDE-1 at product companies, but with a finance-adjacent scope. You work on trading platforms, risk engines, data pipelines, or internal tooling for banking operations. The "analyst" designation is Goldman's standard campus-hire title across all divisions, not a data-analysis role.

Is ECE eligible for Goldman Sachs engineering roles? Yes. Goldman accepts candidates from CS, ECE, EE, and sometimes Maths/Statistics backgrounds. The HackerRank test is the equalizer. If you clear the technical assessment, your branch does not matter for subsequent rounds.

What is the typical CTC for Goldman Sachs Engineering Analyst in India? The candidate did not disclose compensation. Goldman Sachs Analyst CTC in India for 2025 hires is typically in the range of ₹17 to ₹25 LPA depending on location (Bangalore vs. Hyderabad), division, and market conditions. This includes base salary, signing bonus, and annual bonus.

How long is the Goldman Sachs interview day on campus? In this specific cycle: 10.5 hours. The HackerRank test runs first (typically morning or early afternoon), followed by back-to-back interviews for shortlisted candidates. The entire process happens on the same day. There is no multi-day scheduling for campus drives.

Also on PapersAdda

• FAANG lateral comparison: Amazon SDE-2 (L5) 2026: A Tier-3 Engineer's Real 5-Round Loop to ₹60L, the lateral hiring equivalent at Amazon
• Another campus-to-offer pipeline: Amazon SDE-1 Onsite 2026: Tier-2 Engineer's Real 4-Round Loop, a campus-adjacent hire with a comparable round structure
• The Google L4 counterpoint: Google L4 Bangalore April 2025: Topological Sort to 12-Month Cooldown, what happens when the loop does not go your way
• Flipkart machine coding comparison: Flipkart SDE-2 Bangalore 2024: Wallet Machine Coding to Offer, a different machine coding format at a product company

Sourcing and accuracy: the figures here are candidate-reported estimates, not official numbers. Confirm on the official portal before you make decisions, since vacancy counts, cut-offs, and patterns vary by cycle and by role.