Goldman Sachs runs on a stack you have almost certainly never touched: a single object database called SecDB that models the firm’s entire risk, and a proprietary scripting language, Slang, that thousands of people write against every day. The firm has been migrating to a Python-based replacement called Atlas for years now, but the point stands. Goldman employs north of 12,000 engineers, more than most of the AI labs, and a large share of them do software in a way no product company does. The interview looks like everyone else’s on the surface. What it is really checking is whether you can do serious engineering inside a bank.
So before anything else, know which door you are walking through, because Goldman splits technical hiring into two tracks and you apply to one.
Engineering and Strats are two different jobs
Software Engineering, the division most people mean by “tech,” builds the platforms: trading and risk systems, the Marquee client APIs, data infrastructure, the internal tooling that ten thousand colleagues depend on. Strategists, universally called strats, sit next to the business. Desk strats price derivatives and manage risk shoulder to shoulder with traders. There are also strats in risk, in core quant modeling, and in engineering-adjacent roles. A strat is a quant developer. You need the math and the code, and you get judged on both.
The loops overlap but the weighting is different. A pure SWE loop leans on data structures, some system design, and a behavioral read. A strat loop keeps the coding and adds probability, expected-value questions, and a math-heavy conversation about statistics or a pricing problem. If you are strong at coding but rusty on brainteasers, apply to engineering. If you like the math and want to sit near a trading desk, strats fits better, and desk-strat comp tends to run higher because it tracks the business more directly.
The process: HackerRank, screens, then a Superday
The front door for most tech roles is an online assessment on HackerRank. Two to three problems, roughly 60 to 90 minutes, auto-graded on hidden test cases. For strats the assessment often bolts on a set of probability and math multiple-choice items, so a coding-only prep plan leaves points on the table. Early-career candidates frequently get a HireVue on top: a recorded video round with a few behavioral prompts and sometimes a light technical question, done on your own time.
Clear the assessment and you get one or two live technical screens with an engineer or strat, usually 45 to 60 minutes in a shared editor, part coding and part walking through something on your resume. The final round is the Superday. Historically a full day of back-to-back interviews in the NYC office, now often virtual or a hybrid, it runs three to five sessions covering coding, a design or systems discussion, behavioral, and for strats a probability block. The name is the bank’s, not mine, and it is exactly what it sounds like.
| Stage | Format | What it tests | Typical timing |
|---|---|---|---|
| Online assessment | HackerRank, 2-3 coding problems; strats also get probability/math items | Data structures, correctness under a timer | 60-90 min, auto-graded |
| HireVue (early-career) | Recorded one-way video, behavioral plus a light technical prompt | Communication and motivation, “why Goldman” | 20-30 min, on your own schedule |
| Technical screen | 1-2 live calls with an engineer or strat in a shared editor | Coding, resume deep-dive, how you reason out loud | 45-60 min each |
| Superday (final round) | 3-5 back-to-back interviews, virtual or onsite | Coding, design discussion, behavioral; probability for strats | Roughly half a day |
What the coding rounds actually ask
The bar is not FAANG-brutal. Think LeetCode easy to medium, weighted toward arrays, strings, hash maps, and the occasional two-pointer or simple dynamic programming problem. The HackerRank grader wants all test cases green, so edge cases and off-by-ones cost you real points in a way a human interviewer might forgive. Get the brute force working first, then optimize if there is time. A correct O(n²) beats a broken O(n).
The phrasing you tend to see:
- “Given an array of daily prices, find the maximum profit from one buy and one sell.”
- “Return whether a string of brackets is balanced.”
- “Count the pairs in an array that sum to a target.”
- “Given a list of trades, compute the running position and flag when it goes negative.”
That last kind, with a finance flavor bolted onto a standard problem, shows up more in strat and desk-facing loops. The underlying algorithm is still a hash map or a prefix sum. Nobody expects you to know derivatives pricing to answer it.
Probability and brainteasers, mostly for strats
This is the block that separates people who prepped for Goldman specifically from people who ground LeetCode and hoped. Strat interviews reliably include expected-value and probability questions, asked conversationally, where they care about your reasoning as much as the number. A few in the wild:
- “Expected number of fair-coin flips to get two heads in a row.” (The answer is 6, and they want to see you set up the recurrence.)
- “You roll a die until you get a 6. What is the expected sum of all your rolls?”
- “A hundred passengers board a plane; the first sits in a random seat and everyone after takes their own seat or a random one if it’s taken. Probability the last passenger gets their assigned seat?” (One half.)
- “Draw two cards from a shuffled deck. Probability both are aces?”
The airplane one and the two-heads recurrence are close to canon in these loops. Work through a real quant probability set until conditional expectation and simple recurrences feel automatic. If you freeze on setting up states, that is the specific weakness to drill.
System design, bank-flavored
Goldman’s design questions skew away from “design Twitter” and toward systems that have to be correct and auditable. Design a service that ingests a market data feed and computes positions in real time. Design an order management system and reason about what happens when a message is dropped or duplicated. Talk about idempotency, ordering guarantees, and how you would reconcile state after a failure, because in a trading system a lost or double-counted message is money and a regulator’s phone call, not a stale timeline.
For senior SWE loops you will also get ordinary distributed-systems material: caching, queues, database choices, consistency trade-offs. The tell that you have thought about this domain is talking about correctness and recovery before you talk about scale. A candidate who opens with “how do we make sure we never lose or replay a fill” is speaking the language of the desk.
What the engineering culture is genuinely like
The proprietary stack is the defining feature and the thing recruiters undersell. SecDB, short for Securities Database, is a firm-wide object graph that models positions and risk, and strats script it in Slang. It is genuinely powerful and genuinely unlike anything you learn in school or at a startup, which means your first months are spent learning a system you cannot Google your way through. Atlas, the Python-based successor, is where a lot of new work goes, so modern Python skills travel well even into the strat world. On the platform side, Marquee exposes the firm’s pricing and risk to clients over web and API, and GS Quant is the open-source Python toolkit built on top of it, which is worth cloning before an interview so you can speak to it.
The real trade-off is that this is a bank. Regulation, change control, and audit trails are real constraints, deploys are more careful than at a product company, and some of your work is plumbing that keeps a regulated business running rather than a shiny feature. In exchange you get problems with actual stakes, unusually strong mentorship in the strat groups, and exposure to how markets and risk work that you cannot get anywhere else. Engineers who thrive there like the depth and do not mind that “move fast and break things” is, for good reason, not the operating model when the thing that breaks is a trading book.
Comp: competitive base, bonus is the wild card
Base salaries are competitive, especially at junior levels, but total compensation trails Big Tech at the senior end, and the reason is structure. Reported new-grad analyst base in NYC tends to land somewhere in the low six figures, with associates higher and VPs higher again. The variable is the bonus, which is discretionary, swings hard with the firm’s year, and at senior levels arrives partly as deferred Goldman stock that vests over several years. That deferral is the core difference from a FAANG offer, where a large slice of comp is liquid RSUs from day one.
So a Goldman VP and a big-tech senior engineer can post similar headline numbers while the cash-versus-deferred mix and the volatility look nothing alike. Do not anchor on a single Blind screenshot. Pull the current ranges from levels.fyi, cross-check Wall Street Oasis for the banking-specific view of base and bonus by title, and in the offer conversation ask directly how the bonus splits between cash and deferred stock and what the vesting schedule looks like. That last question tells you more about the real value than the total figure does.
How to prep without wasting weeks
Do timed HackerRank problems, not untimed LeetCode. The interface, the auto-grader, and the pressure of hidden test cases are the actual test, and getting comfortable with that environment is worth more than grinding a hundred hard problems in a nicer editor. Cover arrays, strings, hash maps, two pointers, and basic dynamic programming, and practice getting a working answer out fast rather than chasing the optimal one under a clock.
If you are going for strats, budget at least as much time on probability as on coding. Expected value, conditional probability, and simple recurrences should be reflexes. For system design, read up on how exchanges and order management systems handle ordering and failure, so you can talk about idempotency and reconciliation without hand-waving. And prepare real “why Goldman, why this division” answers, because in a bank behavioral rounds carry more weight than they do at a startup, and “I want to work on hard problems” is not an answer. Know that SecDB, Slang, Atlas, and Marquee exist and be able to say a sentence about each. Walking in aware that you are joining a firm with its own language, rather than acting surprised by it, is a quiet signal that you did the homework the rest of the field skipped.
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