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Citadel Software Engineer Interview Guide 2026

This guide covers Citadel's 2026 software engineer interview process, detailing live technical coding interviews, systems fundamentals, API and......

Topics: Citadel, Software Engineer, interview guide, interview preparation, Citadel interview

Author: PracHub

Published: 3/21/2026

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Citadel Software Engineer Interview Guide 2026

This guide covers Citadel's 2026 software engineer interview process, detailing live technical coding interviews, systems fundamentals, API and......

5 min readUpdated Jul 1, 202643+ practice questions
43+
Practice Questions
4
Rounds
7
Categories
5 min
Read
Contents
TL;DRSample QuestionsAbout the Interview ProcessWhat to expectInterview roundsRecruiter or screening stageOnline assessment or recorded pre-screenFirst roundVirtual onsite (second round)Leadership or team-match interviewFinal reviewWhat they testCoding and algorithmsReasoning and systems fundamentalsYour resumeMotivation and operating styleHow to stand outHow to Use This Page as a Prep PlanFAQHow should I use this guide?What should I do if I am short on time?How do I know I am ready?
Practice Questions
43+ Citadel questions
Citadel Software Engineer Interview Guide 2026

TL;DR

Citadel's Software Engineer process is built around live technical interviews, but it is more varied than the old "just LeetCode" reputation suggests. A typical pipeline opens with a 45-minute first-round technical interview, followed by a virtual onsite that usually consists of three separate 45-minute interviews, then one or more team-specific conversations and an internal final review. Coding remains the core early filter, but systems fundamentals, design depth, and resume-based architecture discussions come up more often than many candidates expect - especially for experienced hires. A distinctive part of Citadel's process is team matching. Clearing the general technical rounds is not the end: your performance still has to line up with a specific team's needs, domain, and location before an offer comes together. Depending on the role and office, you may also encounter a recruiter screen, a recorded screening step, or an online assessment before the live interviews. Exact round counts and formats vary by team and level, so treat the structure below as the common shape rather than a fixed script.

Interview Rounds
HR ScreenOnsiteTake-home ProjectTechnical Screen
Key Topics
Coding & AlgorithmsSystem DesignBehavioral & LeadershipSoftware Engineering FundamentalsML System Design
Practice Bank

43+ questions

Estimated Timeline

2–4 weeks

Browse all Citadel questions

Sample Questions

43+ in practice bank
System Design
1

Design a low-latency trading system

HardSystem Design

System Design: Low-Latency Electronic Trading Platform (Equities)

You are designing a single-region electronic trading platform (exchange/ATS-like) that supports market and limit orders for equities. Clients are co-located in the same region, and the system must deliver high determinism, strict auditability, and robust failure handling.

Work through the parts below. Each part is a distinct design area; a strong answer treats them as one coherent system rather than ten isolated features — call out where a decision in one part constrains another (for example, how ordering in Part 1 relates to replay in Part 6, or how the durability choice in Part 6 spends the latency budget in Part 5).

Constraints & Assumptions

Anchor your design to these figures (state any you'd renegotiate with the interviewer):

  • Throughput: 200,000 orders/second steady-state peak; bursts may exceed peak.
  • Latency SLO: $P99$ end-to-end acknowledgement $< 5\text{ ms}$ within one region.
  • Recovery: $\text{RPO} \approx 0$, $\text{RTO} < 1\text{ minute}$.
  • Instrument: equities; tick size $$0.01$; orders are day-only (extend order types as needed).
  • Clients: authenticated institutional participants, co-located in the same region.
  • Topology: one production region with multiple availability zones; an optional warm-standby region for disaster recovery.
  • Priorities: determinism, auditability, and correctness under failure are non-negotiable; latency is the lever that may degrade gracefully under overload.

Clarifying Questions to Ask

Before designing, scope the problem with the interviewer:

  • Is this the venue/matching engine itself (system of record for the book) or a buy-side client connecting to an exchange? (These are very different systems.)
  • What order types and time-in-force must be supported beyond plain market/limit — IOC, FOK, Post-Only, stop orders? Are amends/replaces in scope?
  • Are clients truly co-located (cross-connect, kernel-bypass on the hot path), or must we also serve WAN participants?
  • For RPO≈0, must durability be synchronous across availability zones, or is AZ-local-synchronous with async cross-AZ replication acceptable? (This single choice has a large effect on the latency budget.)
  • What regulatory regime governs price bands and audit (e.g. LULD-style limit-up/limit-down, clearly-erroneous rules, reconstruction requirements)?
  • Is the 5 ms $P99$ measured gateway-in to ack-out, and does it include the durability commit?

What a Strong Answer Covers

The interviewer is looking for these signals — dimensions to engage with, not the answers themselves. State your position on each tension; there is more than one defensible resolution:

  • A clear hot-path definition (order in → risk → ordering → match → ack out) and a deliberate separation of the latency-critical path from everything else.
  • A concrete order-book data model and a matching algorithm that correctly expresses price–time priority, including the classic edge cases (execution price, market order into a thin book, amend priority, self-trade prevention).
  • A clear stance on how ordering, persistence/replay, and failover relate: do time priority, the audit/replay record, and standby recovery derive from one shared mechanism or from separate ones — and what does your choice cost or buy?
  • A quantified latency budget that adds up to the SLO, with each line tied to a concrete mechanism, and an honest account of the durability vs. latency tension.
  • A coherent account of exactly-once execution reports over a network that gives weaker delivery guarantees — what layer the guarantee actually lives at, and what it costs.
  • A position on how much of the pipeline is reproducible — which stages can be made deterministic, which cannot, and what that implies for audit, replay/backtest, and failover.
  • Explicit, surfaced tradeoffs (multi-AZ-synchronous RPO vs. tail latency, cr
View full question
2

Design stock price time-series store and query

EasySystem DesignPremium
View full question
Coding & Algorithms
3

Implement task queue with insert, delete, execute

MediumCoding & AlgorithmsCoding

Problem: Task manager with insert/delete/execute-next

Design a data structure to manage executable tasks.

Each task has:

  • taskId (unique)
  • priority (higher means executed earlier)
  • createdAt (or seq) to break ties among equal priority (earlier first)

Support operations:

  1. add(taskId, priority)
  2. remove(taskId) (task may or may not exist; removing a task prevents future execution)
  3. executeNext() -> taskId | null
    • Returns and removes the highest-priority remaining task.
    • Tie-breaker: smaller createdAt/seq first.

Requirements

  • Aim for O(log n) per operation.
  • You may not linearly scan all tasks on executeNext().

Notes

  • Explain how you handle remove() efficiently even if the task is not at the top of the heap when removed.
View full question
4

Implement LRU/LFU cache with custom eviction

EasyCoding & AlgorithmsCodingPremium
View full question
Software Engineering Fundamentals
5

Design a Thread-Safe Shared Counter

MediumSoftware Engineering Fundamentals

Design and implement a per-key call counter: a component that tracks how many times each distinct key has been seen.

The API is a single method, for example:

long incrementAndGet(String key)

Each call increments the count associated with key and returns the new count for that key — i.e. how many times that key has been passed to the method so far, including this call.

Work through the design in increasing order of difficulty across the three Parts below.

Constraints & Assumptions

  • Single host throughout. Part 3 is cross-process on one machine, not a distributed/multi-host system (no global clock or network partitions to reason about).
  • Keys are strings; the key space may be large and is not known up front. Treat it as potentially unbounded unless you state otherwise.
  • Each incrementAndGet must return the exact count after this call, not an approximate or eventually-consistent total.
  • Counts fit in a 64-bit signed integer; overflow is out of scope.
  • Unless you argue otherwise, in-memory state is acceptable for Parts 1–2; durability across restarts is a concern you may raise in Part 3.

Clarifying Questions to Ask

  • What is the expected call rate and the read/write ratio — is this low-throughput, or a hot path with heavy contention?
  • How large is the key space, and do keys need to be evicted/expired, or do counts live forever?
  • Must counts survive a process restart or host reboot (durability), or is in-memory state acceptable?
  • Is introducing an external dependency (a database, a cache server) allowed in Part 3, or must the solution be self-contained?
  • Does the returned count have to be exact and strictly monotonic per key, or is an approximate/eventually-consistent total acceptable for some callers?

Part 1 — Basic in-process counter

Implement the counter for a single-threaded, in-process caller. Define the backing data structure and the incrementAndGet logic, and state explicitly why this version is not safe once multiple threads call it concurrently.

A map from key → count is the natural backing store. The interesting part is what breaks when the read-modify-write (read the current value, add one, write it back) is interleaved across threads.

What This Part Should Cover

  • A correct single-threaded baseline: a map from key to a 64-bit count, with an unseen key defaulting to 0.
  • A precise account of why it breaks under concurrency — the read-modify-write is three separate steps, not one atomic action, so increments can be lost.
  • Recognition that the underlying non-thread-safe map can itself be corrupted (not just produce wrong counts), independent of the counting logic.

Part 2 — Make it thread-safe, and compare approaches

Make incrementAndGet correct under concurrent calls from many threads, on both the same key and different keys. Then compare at least two or three distinct approaches and articulate the trade-offs (correctness, contention/throughput, memory, complexity). State which you would choose by default and why.

The whole read-modify-write must be atomic. A single coarse lock (a `synchronized` method or one `ReentrantLock`) is the simplest correct version — use it as your baseline, then argue about its cost.

A coarse lock serializes *unrelated* keys against each other. Can you make the unit of atomicity per-key instead of global, so updates to different keys never block one another? Think about what each key's value would have to be for its own increment to be atomic without a shared lock.

"Check if a key is absent, then insert a new counter" is two operations and races — two threads can each create a separate counter for the same key. How could the find-or-create step be collapsed into one atomic action? And once you settle on a per-key accumulator, check whether it can return the *exact* post-increment value or only an event
View full question
6

Explain JS types, Promises, Maps, WebSockets

MediumSoftware Engineering Fundamentals

JavaScript Types, Promises, Collections, and WebSocket Ordering

You are interviewing for a software engineering role. Answer the following about JavaScript runtime behavior, collections, and networking:

1) Data Types and Data Structures

  • List JavaScript primitive types vs reference types.
  • Name common built-in data structures used in practice.
  • Which values coerce to false in a boolean context?

2) Promises: Return Values and Error Propagation

  • What do then/catch/finally return?
  • What happens if an error is thrown in the Promise executor or in any handler (including inside an async function)?

3) Plain Objects vs Map

Compare by:

  • Key types allowed
  • Iteration order
  • Default/inherited properties and collision issues
  • Performance characteristics (adds/removes/lookups)
  • Typical use cases

4) WeakMap

  • What is it?
  • How do its key constraints and garbage-collection behavior differ from Map?

5) WebSockets and Message Ordering

  • Does a single WebSocket connection guarantee in-order, reliable delivery?
  • When can ordering be affected (e.g., multiple connections, reconnections, server fan-out)?
  • How would you design for ordering and/or idempotency if required?

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

  • Clarify the goal, inputs, constraints, stakeholders, and success criteria.
  • State assumptions before using them.
  • Keep the answer grounded in the prompt rather than adding outside facts.

What a Strong Answer Covers

  • A structured framing of the problem and constraints.
  • A concrete approach with trade-offs and edge cases.
  • A way to validate the answer and communicate the recommendation.

Follow-up Questions

  • What assumption is most important to validate first?
  • What could make the answer fail in practice?
  • How would you explain the result to a non-technical stakeholder?
View full question
Behavioral & Leadership
7

Explain role, motivations, values, and relocation expectations

EasyBehavioral & Leadership
Question

This is a Citadel HR/recruiter screen for a Software Engineer role. The recruiter walks through a standard set of behavioral and logistics questions:

  1. Give a brief self-introduction and describe your current role and your team's responsibilities (mission, scope, tech stack, impact).
  2. Why are you considering leaving your current company?
  3. What do you value most in a company when evaluating opportunities?
  4. Are you willing to relocate to New York City? If so, what total compensation (including base, bonus, and relocation support) would make relocating the right decision for you?

Approach: This is a recruiter screen scored on communication, self-awareness, motivation/fit, and realistic relocation + compensation logistics — not technical depth. The rubric: a crisp Present→Past→Future intro with quantified impact; pull-based (not push-based) reasons for leaving; three or four company values backed by evidence; a direct relocation answer with timeline/constraints; and a total-comp range (base + bonus + relocation) with flexibility and good clarifying questions. Tailor framing to Citadel as a quant/hedge-fund employer (high-bar low-latency systems; large discretionary bonus rather than heavy public equity).

View full question
8

How do you handle conflict at work?

MediumBehavioral & Leadership

Describe a time you had a conflict with a teammate (e.g., disagreement on technical direction, priorities, code quality, or ownership).

Please cover:

  • What was the context and what was at stake?
  • What exactly was the disagreement?
  • What actions did you take to resolve it?
  • What was the outcome and what did you learn?

Follow-ups:

  • How do you handle conflict when you’re not the decision-maker?
  • How do you handle conflict when you believe the other person is objectively wrong?
  • What would you do differently next time?
View full question
ML System Design
9

Build models for housing and wind power prediction

HardML System Design

Two-Part Machine Learning Take-Home

Part 1 — Binary Classification: "Can Buy" vs "Cannot Buy"

Given applicant and market data, design a binary classifier to predict whether an applicant can buy a house (labels: can buy, cannot buy). Specify the following:

  1. Data assumptions and label definition.
  2. Preprocessing steps (missing values, outliers, leakage guards, encoding, scaling).
  3. Feature design (engineered affordability features, credit/market features).
  4. Model choice and rationale (including interpretability/calibration if applicable).
  5. Validation strategy (splits, class imbalance handling, threshold selection).
  6. Evaluation metrics and decision thresholding (including business-aligned metrics).

Part 2 — Regression: Wind-Farm Power Output

You are provided three files: train.csv, test.csv, sample_submission.csv. The target column is power output in train.csv. For each record in test.csv, predict power output and produce a submissions.csv with exactly two columns and a header: id, power output.

Describe your end-to-end approach:

  1. Data checks and exploratory analysis (schema, leakage risks, target sanity checks).
  2. Feature engineering (physics-aware, statistical, temporal, and interaction features).
  3. Time-aware validation to avoid leakage (rolling/sliding windows; lag construction).
  4. Model selection and tuning approach (baselines through advanced models).
  5. Metrics (primary/secondary) and error analysis.
  6. Training and inference pipeline, including how you would generate submissions.csv.
View full question
Machine Learning
10

Choose models for trading tasks

HardMachine LearningPremium
View full question
Statistics & Math
11

Compute Statistics from a Frequency Array

HardStatistics & Math

You receive an array freq of length 256. freq[v] is the number of times integer value v occurs in an implicit data set. For example, [2, 5, 8] represents two zeroes, five ones, and eight twos.

Without expanding the implicit data set, explain how you would compute its count, minimum, maximum, arithmetic mean, median, mode, and population standard deviation. Give the time and extra-space complexity and define the behavior for an empty data set.

Constraints & Assumptions

  • Every frequency is a non-negative integer.
  • The total count and weighted sums may require wider numeric types than an individual frequency.
  • If multiple values share the largest frequency, report the smallest as the mode.
  • For an even count, the median is the average of the two middle observations.
  • Return “undefined” for value-based statistics when the total count is zero.

Clarifying Questions to Ask

  • Is population or sample standard deviation required?
  • How should ties for the mode be resolved?
  • What numeric precision and overflow behavior are expected?
  • What should the API return for an empty data set?

Solving Hints

  • Most statistics are weighted sums or positions in cumulative frequency.
  • The two median ranks can be located during a single cumulative scan.
  • A variance formula based on two weighted moments avoids materializing samples.

What a Strong Answer Covers

  • Correct formulas and zero-based or one-based median-rank handling.
  • A clear empty-input contract and deterministic mode tie-breaking.
  • Overflow and floating-point precision considerations.
  • O(256) time and O(1) extra space, generalized to O(m) for m bins.
  • An explanation of why the implicit data set must not be expanded.

Follow-up Questions

  1. How would you merge statistics from frequency arrays computed on separate machines?
  2. When can E[X^2] - E[X]^2 lose numerical precision, and what alternative would you use?
  3. How would the algorithm change if values were arbitrary sparse integers rather than 0..255?
  4. How would you compute a requested percentile?
View full question

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About the Interview Process

What to expect

Citadel's Software Engineer process is built around live technical interviews, but it is more varied than the old "just LeetCode" reputation suggests. A typical pipeline opens with a 45-minute first-round technical interview, followed by a virtual onsite that usually consists of three separate 45-minute interviews, then one or more team-specific conversations and an internal final review. Coding remains the core early filter, but systems fundamentals, design depth, and resume-based architecture discussions come up more often than many candidates expect - especially for experienced hires.

A distinctive part of Citadel's process is team matching. Clearing the general technical rounds is not the end: your performance still has to line up with a specific team's needs, domain, and location before an offer comes together. Depending on the role and office, you may also encounter a recruiter screen, a recorded screening step, or an online assessment before the live interviews. Exact round counts and formats vary by team and level, so treat the structure below as the common shape rather than a fixed script.

Citadel Software Engineer Interview Guide 2026 visual study map Visual study map Coding correctness, edge cases Design APIs, data, scale Engineering debugging, tradeoffs Behavioral ownership and values Use this map to decide what to practice first, then check each area against the examples in the guide.

Video companion: This verified YouTube video gives a second pass on the same prep area.

Interview rounds

Recruiter or screening stage

When this stage is a live call, it usually lasts 20 to 30 minutes; some candidates instead report a one-way recorded screening. Either way, it checks basic role fit, communication, motivation for Citadel, and logistics such as office, level, and compensation. Be ready to walk through your resume, explain what you have built, and describe a difficult technical problem you solved and how you approached it.

Online assessment or recorded pre-screen

Not every pipeline includes this step. When it appears, reported formats include a timed coding assessment and, in some cases, systems-fundamentals questions on topics like concurrency, threads, and locks. The goal here is usually raw speed, correctness, and your ability to stay accurate under time pressure.

First round

The first round is typically a 45-minute live video interview that pairs technical problem solving with a short behavioral component. The emphasis is on coding fluency, data structures and algorithms, edge cases, and your ability to explain trade-offs rather than jumping straight into implementation. Expect a brief discussion of your projects, internships, or reasons for applying as well.

Virtual onsite (second round)

The next stage is usually a virtual onsite of three separate 45-minute interviews, often run back-to-back. Across these, Citadel evaluates consistency, coding correctness and speed, systems thinking, communication, and how you handle follow-up questions under pressure. A common pattern is two coding-heavy interviews plus a more design- or systems-oriented discussion, with behavioral questions mixed in.

Leadership or team-match interview

If a team is interested, you typically meet a senior engineer or hiring manager in a team-specific round. This stage leans less on pure algorithm drills and more on ownership, judgment, engineering depth, and how well your background fits the team's domain and working style. Expect to discuss systems you have built, architecture and trade-off decisions, and what it is like to work with researchers, investors, or trading-facing stakeholders.

Final review

The final review is usually an internal decision stage rather than another formal interview. Here Citadel weighs your technical signal, team fit, business need, and location alignment. The outcome may be an offer, additional team conversations, or a decision that the fit is not strong enough.

What they test

Coding and algorithms

Citadel tests the standard core of software engineering interviews hard: data structures, algorithms, and coding under time pressure. Be comfortable with arrays, strings, hash maps, trees, heaps, graphs, recursion, dynamic programming, greedy methods, sliding-window patterns, and graph traversal. The bar is not just "eventually solve it." You need to clarify the problem, choose a reasonable approach, write correct code quickly, and talk through edge cases, complexity, and optimization without losing composure.

Reasoning and systems fundamentals

What makes Citadel more specific is the weight it puts on reasoning quality and practical engineering judgment. You will likely be pushed on trade-offs, not just final answers. Systems fundamentals matter more than many candidates assume - particularly concurrency, threading, locking, memory behavior, and performance. Experienced engineers should also expect deeper questions on production and distributed systems, reliability, observability, debugging, and low-latency or performance-sensitive design.

Your resume

In later rounds, your background becomes part of the test. Be ready to defend architecture decisions, explain scaling bottlenecks, and show that you understand not just how a system worked but why the design fit a real business need.

Motivation and operating style

Citadel also screens for fit and motivation. Expect questions about why this environment appeals to you, why finance or trading-adjacent engineering interests you, and how you have handled ambiguity, accountability, and cross-functional collaboration. The interviewers tend to value engineers who connect technical work to commercial outcomes rather than treating engineering as an isolated craft.

How to stand out

  • Frame the problem before you code. State assumptions, ask clarifying questions, and outline your approach first. Citadel values careful reasoning over rushing.

  • Practice under a realistic 45-minute cap. Work medium-to-hard problems against the clock so slow starts don't cost you, and expect interviewers to add follow-ups once you reach a working solution.

  • Prepare for design and modeling prompts, not just algorithm drills. Some questions test how you model state, APIs, and trade-offs rather than whether you know a single pattern.

  • Review systems fundamentals closely. Concurrency, threads, and locks are worth refreshing even if your live interviews skew coding-heavy, since some pipelines test these in a pre-screen.

  • Bring one sharp architecture story. Pick a system from your resume you can explain end to end: requirements, design choices, bottlenecks, failure modes, observability, and what you would change in hindsight.

  • Show commercial awareness. Connect technical decisions to latency, reliability, user impact, or business outcomes - Citadel cares about engineering that supports real trading and investment workflows.

  • Manage pressure out loud. If you get stuck, narrate your thinking, propose alternatives, and recover in real time. Freezing or going silent reads worse here than openly reasoning through uncertainty.

How to Use This Page as a Prep Plan

Do not treat this as passive reading. Convert the ideas in this page into a short weekly loop: learn one idea, practice it under interview conditions, then write down what changed. That is the fastest way to turn advice into visible interview behavior.

Prep areaWhat you need to provePractice artifact
UnderstandTurn the prompt into a concrete goal.Clarifying questions and success criteria.
PracticeUse realistic constraints and timed reps.Worked examples with edge cases.
ExplainMake reasoning visible.Tradeoffs, assumptions, and test strategy.
ImproveReview misses quickly.A short feedback log and next action.

For Citadel Software Engineer Interview Guide 2026, the strongest candidates usually do three things well: they make their assumptions explicit, they use concrete examples instead of vague claims, and they review mistakes quickly enough that the next practice rep is better than the last one.

FAQ

How should I use this guide?

Read it once for the structure, then turn each section into a practice task with a visible artifact.

What should I do if I am short on time?

Prioritize the skills most likely to be tested, then do one mock or timed drill to expose the largest gap.

How do I know I am ready?

You can explain your approach clearly, recover from hints, and name tradeoffs without relying on memorized wording.

Frequently Asked Questions

It is hard, but not in a vague horror-story way. The bar feels high because they move fast, expect clean thinking, and care about both coding ability and how you reason under pressure. I found it tougher than a standard big tech loop because weak spots show quickly, especially in algorithms, systems, and communication. The questions themselves are usually fair, but the pace is intense. If you are strong technically and can stay calm while explaining your choices, it feels demanding rather than impossible.

My process had the usual shape: an initial recruiter screen, then a technical phone or online assessment, then deeper interviews that covered coding, problem solving, and system design or low-level design depending on level. There was also strong attention on past projects, impact, and how I made engineering decisions. Some candidates also see a hiring manager or team-match style conversation near the end. The exact order can vary, but expect multiple technical rounds and expect interviewers to push beyond the first working answer.

If you already do competitive coding or interview-style problems regularly, a few focused weeks can be enough. If you are rusty, give yourself six to ten weeks. That was the difference for me between barely remembering patterns and being able to solve problems while talking clearly. I would not just grind random LeetCode. Split time across coding, data structures, debugging, concurrency basics, and explaining past work. The biggest jump comes from timed practice and mock interviews, because the real process feels fast and very interactive.

Algorithms and data structures matter a lot: arrays, graphs, trees, heaps, hash maps, recursion, dynamic programming, and complexity tradeoffs. Beyond that, I would put real weight on writing clean code, testing edge cases, and explaining why your solution is safe and efficient. For software engineer roles, systems topics also matter more than people expect: concurrency, memory, networking basics, distributed systems ideas, and performance thinking. They also care about whether you have actually built things, so be ready to discuss design choices, failures, and what you would improve.

The biggest mistake is going silent and treating the interview like a private contest. They want to hear how you think. Another bad one is jumping into code before locking down assumptions, inputs, and edge cases. I also saw people hurt themselves by forcing fancy solutions when a simpler one was easier to justify. Weak debugging habits stand out fast. On the behavioral side, vague project stories do not land well. You need specifics: what you owned, what broke, what tradeoffs you made, and what the result actually was.

CitadelSoftware Engineerinterview guideinterview preparationCitadel interview

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