Optiver Software Engineer Interview Guide (2025) – Process, OA Questions & Salary Insights

The Optiver software engineer interview sets the stage for evaluating your ability to design and optimize systems that process millions of market events per second. Candidates tackling Optiver software engineer interview questions should be ready to showcase expertise in concurrency, low-latency networking, and performance tuning—skills every Optiver software engineer relies on daily. In this guide, we’ll unpack what to expect at each stage of the process, the types of problems you’ll face, and how best to prepare.

Beyond technical chops, Optiver looks for engineers who thrive in small, autonomous squads where you’ll build features end-to-end, deploy to production, and own the results. Whether you’re optimizing a data feed handler or architecting a fault-tolerant risk service, understanding the interview flow is your first step toward excelling.

Role Overview & Culture

At Optiver, software engineers collaborate closely with traders and quantitative researchers to deliver ultra-fast, reliable trading platforms. You’ll work in agile pods that value “build-and-own” responsibility, meaning you’re involved from design through deployment and ongoing maintenance. The culture emphasizes speed without sacrificing quality—your code must be both safe and performant under real-world market pressures. Regular code reviews, paired programming sessions, and post-mortems ensure continuous learning and improvement across the engineering organization.

Why This Role at Optiver?

Joining Optiver means your work directly impacts global markets and influences multi-million-dollar decisions in real time. The compensation package reflects the outsized responsibility, with competitive base pay, performance bonuses, and rapid promotion tracks for high performers. You’ll gain exposure to cutting-edge technologies—such as Rust for systems programming or Kafka for streaming data—and contribute to open-source tools used industry-wide. Ready to see how your skills align? Let’s dive into the Optiver software engineer interview process and explore how to position yourself for success.

The Optiver software engineer interview process begins with a holistic evaluation of both your problem-solving abilities and your fit for a high-velocity trading environment. You’ll progress through increasingly technical stages that test coding fluency, system-design prowess, and collaboration under time constraints. Understanding each step helps you tailor your preparation and demonstrate the ownership and performance Optiver demands.

Application → Recruiter Screen

Your journey starts with submitting your résumé and completing a brief phone call with a recruiter. They’ll assess your background in systems programming or low-latency development, clarify role expectations, and review logistics like location and timing.

Online Assessment (OA)

Next, you’ll tackle an 80-minute, HackerRank-style quiz that combines algorithmic problems with domain-relevant tasks. This Optiver online assessment gauges your coding efficiency and familiarity with data structures under timed conditions, mirroring the pace expected on the job.

Technical Phone / Live Coding

In a live coding session, you’ll pair-program with an engineer on a shared IDE, solving real-world challenges such as concurrent data ingestion or micro-service routing. Be prepared for the Optiver live coding interview, where clear thought processes and defensive coding count as much as a correct solution.

On-Site / System Design Loop

During the on-site loop, you’ll delve into multi-layered architecture scenarios—designing failover strategies, sketching low-latency pipelines, or optimizing cache strategies. This is where the Optiver system design interview shines a light on your ability to build scalable systems that meet stringent performance SLAs.

Final Debrief & Offer

After completing all technical rounds, feedback is consolidated quickly—often within days—and shared during a final discussion that covers compensation, team match, and career-level expectations.

Behind the Scenes

Behind the scenes, recruiters use the Optiver OA performance as an initial screening metric, while Engineering leads coordinate panel scores to ensure consistency. Fast internal alignment means you’ll often hear back within a week of your on-site.

Differences by Level

Interview expectations shift with seniority. Junior candidates focus on writing bug-free, maintainable code, whereas seniors are asked to lead design critiques, mentor others, and justify broader architectural trade-offs—reflected in the Optiver senior software engineer salary band and role scope.

At Optiver, the software engineer interview revolves around real-world scenarios that mirror the low-latency, high-throughput challenges you’ll face on the trading floor. You’ll be evaluated across four key areas—coding proficiency, system architecture, cultural alignment, and your performance in the initial online screen. Below is an overview of what each category examines and how to think about structuring your responses.

Coding / Technical Questions

In this phase, you’ll tackle Optiver coding questions designed to probe your algorithmic fluency and data-structure mastery under time pressure. Expect Optiver hackerrank questions that simulate on-the-job coding tests, as well as a rigorous Optiver coding knowledge test element to verify your ability to write clean, efficient code. Interviewers look for clear trade-offs between time and space complexity, careful handling of edge cases, and explanations that reveal your thought process as you optimize for speed.

1. How would you implement a random-forest classifier completely from scratch under the constraints that all features are 0/1 dummies and each tree must split on every possible feature permutation before voting on a new point?

   Explain that the task assesses low-level algorithmic mastery: you must hand-code bootstrap sampling, exhaustive decision-tree generation, and majority voting using only NumPy / pandas (no scikit-learn). A strong answer details how you would enumerate feature permutations, stop recursion when a leaf is pure, store trees efficiently, and compute the ensemble prediction in O(#trees) time. Discuss trade-offs—exponential tree growth, memory pressure, and why you might prune permutations or limit tree depth to keep latency acceptable in a trading engine.

2. How would you code Dijkstra’s shortest-path algorithm for a weighted graph that’s supplied as a nested-dictionary adjacency map, returning both the distance map and each node’s previous pointer?

   Your explanation should cover priority-queue choice (binary heap from heapq for O(E log V) performance), initialization with infinite distances except the source, relaxation logic inside the loop, and edge-case handling for disconnected vertices. Mention how to reconstruct the path by walking the “previous” map back from any target node and why setting previous[source] = None avoids ambiguity.

3. How can you compute a recency-weighted average salary when the input is a chronologically ordered list of yearly values?

   The interviewer wants to see whether you can translate a verbal weighting rule into mathematics. Describe mapping linear weights such that the most recent year gets weight n, the oldest gets 1, then normalizing those weights before taking a dot product with salary values. Discuss rounding to two decimals, vectorizing with NumPy for O(n) time, and why this emphasises fresh market data in compensation models.

4. How would you write a single SQL query that, for every advertiser with the highest weekly revenue in 2021, returns their three best individual revenue days?

   Outline a two-step window-function approach: (a) use SUM(amount) OVER (PARTITION BY advertiser_id, week) to flag best-revenue weeks, then (b) within those advertisers select the top 3 distinct transaction_dates using ROW_NUMBER() ordered by amount DESC. Emphasise de-duplication, week extraction via DATE_TRUNC(‘week’, txn_ts), and why every amount being unique simplifies tie-breaking.

5. Given friends, page_likes, and blocks tables, how would you identify the single best new friend suggestion for John using a +3/+2 scoring rubric while excluding already-friends or blocked users?

   Explain joining John’s friends to second-degree friends, counting mutuals, adding shared-page-likes, subtracting disqualifiers, and ordering by total score DESC with a LIMIT 1. Note the need for DISTINCT to avoid double-counting mutual friends and why proper indexing on user_id and page_id is crucial for sub-millisecond lookup in production.

6. How would you write a function that returns a random dictionary key with probability proportional to its weight?

   Discuss building a cumulative-weight array, drawing a uniform random float in [0,total), and binary-searching (bisect) the prefix sums for O(log n) selection—versus reservoir or alias methods for frequent sampling. Highlight edge cases: zero weights, very large totals causing float precision, and thread-safety if this utility lives inside a low-latency matching engine.

7. How can you rotate an n × n matrix 90° clockwise in-place, given only a basic Python list-of-lists representation?

   A solid answer describes the layer-by-layer four-way swap (top→right→bottom→left) that achieves O(1) extra space, or shows how transposing then reversing rows also works in O(n²) time. Mention guardrails for non-square matrices (raise error) and why constant extra space matters when the matrix models a large in-memory order-book snapshot.

8. What is an efficient way to find the tail node of a singly linked list, returning None if the list is empty?

   Clarify that you iterate once from head to node.next is None, achieving O(n) time and O(1) space. Touch on defensive coding—checking for head is None, avoiding infinite loops if the list is accidentally cyclic, and how you would unit-test with lists of length 0 and 1.

9. How would you maximise profit from at most two buy/sell transactions given a list of daily stock prices?

   Outline the classic dynamic-programming solution that tracks four states (first_buy, first_sell, second_buy, second_sell) in a single pass, yielding O(n) time and O(1) memory. Explain why nested loops are too slow for real-time back-testing and how this logic maps neatly onto high-frequency trading constraints.

10. How would you convert each integer ≤ 1000 in a list to its Roman-numeral representation?

    Walk through constructing ordered symbol–value pairs (M, CM, D, CD, …) then greedily subtracting the largest possible value until the number hits 0. Detail complexity O(k × m) where k = list length and m = number of symbols, plus the importance of input validation and upper-bound checks to avoid undefined numerals.

11. Which SQL window-function pattern retrieves the last transaction of every calendar day, returning id, timestamp, and amount ordered chronologically?

    Describe using ROW_NUMBER() OVER (PARTITION BY DATE(created_at) ORDER BY created_at DESC) and filtering for row_number = 1, then ordering the outer query by created_at. Emphasise casting or truncating timestamps to dates, indexing on created_at, and why this blueprint generalises to “top-N-per-group” problems common in settlement-feed analytics.

System Design Questions

The Optiver system design segment challenges you to architect robust, low-latency services—think market-data ingestion, order-matching pipelines, or real-time analytics engines. You’ll discuss scalability, fault tolerance, and operational monitoring, all while referencing core Optiver technical interview questions principles such as back-pressure handling and stateful vs. stateless service trade-offs.

1. How would you design a type-ahead search recommendation algorithm for a large streaming-video catalog so that suggestions appear in real time as the user types?

   In a clear answer, outline how you would (1) build an in-memory prefix index (e.g., a trie or a compressed DAWG) that maps keystroke sequences to candidate titles, creators, and genres; (2) develop a lightweight ranking model that blends lexical signals (edit distance, prefix length) with behavioral priors such as historical clicks, session context, and personalization vectors; and (3) create an offline pipeline to refresh embeddings and popularity scores while the online service handles <50 ms latency at global scale. Discuss fallback logic for misspellings, cold-start handling for new titles, A/B-testing success metrics (CTR, keystrokes-to-play), and guardrails to prevent inadvertent content-spoiler leaks.

2. Faced with slow disk writes in a real-time autosave feature, how would you redesign web-based word processor’s persistence layer to increase throughput and cut latency?

   State key assumptions (e.g., doc edits arrive as small diff patches every few seconds). Propose batching edits into append-only logs that flush in the background, caching recent segments in memory, and using write-behind techniques (e.g., double-buffered WALs) to decouple the UI thread from disk I/O. Explain how you’d tier storage—hot shard in NVMe, warm shard in SSD, cold shard in object storage—while guaranteeing exactly-once ordering with version stamps. Finally, describe monitoring (tail latency P99) and a rollback mechanism if the new buffering layer overflows or crashes.

3. Why do OLAP cubes that power monthly and quarterly inventory dashboards often slow to a crawl, and which aggregation strategies can restore sub-minute query times?

   Point to exploding dimensionality—millions of SKU × store × month cells—combined with full-scan group-bys when analysts pivot across quarters. Recommend a pre-aggregation layer (e.g., roll-up tables or materialised views) that stores month-level “chunk” summaries, then uses additive measures to derive quarters quickly. Mention bitmap indexes, columnar partitioning on snapshot_month, and dynamic caching of top-N SKUs. Close with cost trade-offs (additional storage vs. CPU) and note any assumptions, such as immutable historical data.

4. How would you model airports, routes, and hub status in a relational schema so that you can instantly answer questions like “Which hubs lie within 300 km of longitude X/latitude Y?” or “What fraction of flown legs involve at least one hub”?

   Describe a star-ish design: an airports table keyed by IATA code with geo-coordinates and a Boolean is_hub; a routes bridge table capturing origin, destination, distance, and flight_count. Explain adding a spatial index on (lon, lat) for radius searches and a composite (origin, destination) PK (store alphabetically to avoid duplicates). Outline sample queries that aggregate SUM(distance) or calculate hub involvement via CASE WHEN origin_is_hub OR dest_is_hub.

5. If you needed to push comment, mention, and karma notifications in a Reddit-style forum, what would the backend data model and event pipeline look like?

   Sketch tables or Kafka topics for events (user_id, verb, target_id, ts), a subscriptions or “inbox shard” table keyed by recipient, and a Redis/Lua fan-out-on-write cache for unread badges. Address idempotency with monotonic sequence IDs, discuss exponential back-off for mobile push, and explain how you’d archive old notifications into cold storage while keeping the first-page inbox low-latency.

6. Which tables, keys, and constraints would you include in a restaurant-review platform so users can sign up, post one updatable review per venue (optionally with images), and query average ratings fast?

   Propose users, restaurants, reviews (PK = user_id + restaurant_id to enforce one review), review_images, and perhaps a restaurant_stats aggregation table updated via CDC or scheduled jobs. Cover text indexing for search, S3 or CDN hosting for images with signed URLs, and cascading deletes that remove orphaned images if a review is withdrawn.

7. How would you architect a streaming sales-leaderboard dashboard that shows every McDonald’s branch’s real-time revenue, sortable by region and 5-minute window?

   Walk through ingesting POS events into Kafka, aggregating with Flink/Kinesis Analytics into sliding windows, then materialising per-store metrics in a fast store (DynamoDB or Redis Sorted Sets). Explain why you’d push deltas rather than full snapshots to the front-end WebSocket channel, and list resiliency steps—exactly-once semantics, checkpointing, and throttling to avoid UI overload.

8. Design a full relational schema for a stand-alone fast-food restaurant and explain how you’d query yesterday’s top-three revenue items and the drink-attach rate.

   Introduce tables such as orders (order_id, ts, customer_id), order_items (order_id, menu_item_id, qty, price), menu_items (id, name, category, price). Show SQL with SUM(qty*price) grouped by menu_item_id and filtered on DATE(ts) = CURRENT_DATE - 1. For drink attach, compute the ratio of orders that include category = 'beverage'. Emphasise proper indexes on (ts) and (order_id, menu_item_id).

9. If you instrumented license-plate scans at both ends of the Golden Gate Bridge, what schema would capture each traversal and which queries would surface “fastest car today” and “car model with best average time today”?

   Suggest a fact table bridge_crossings with car_id, model, entry_ts, exit_ts, and a computed duration_sec. The fastest-car query selects the min duration_sec where DATE(entry_ts)=CURRENT_DATE. The second query groups by model, takes AVG(duration_sec), and orders ASC LIMIT 1. Highlight time-zone awareness, data skew (rush hour), and a retention policy for high-volume sensor logs.

10. How would you speed up hourly, daily, and weekly active-user counts that refresh every hour when the raw clickstream lives in a data lake?

    Lay out a Lambda/Kappa architecture: raw events land in S3 ➝ Spark Structured Streaming computes distinct-user sets with HyperLogLog sketches ➝ write compact parquet/Hudi tables partitioned by date-hour. Down-sample into daily/weekly roll-ups using Airflow DAGs with backfills, plus a metadata table exposing latest watermark so the dashboard only queries fresh partitions. Mention cost controls: spot instances, object-storage tiering, and pruning by partition predicate.

11. Faced with 600 M Kafka clickstream events per day but only a two-year retention SLA, what low-cost yet queryable analytics stack would you deploy?

    Recommend compressing raw Avro files in S3/Glue, then clustering recent 30 days in ClickHouse or BigQuery for ad-hoc analysis while archiving older data as Z-order–sorted iceberg partitions. Use federation or tiered storage to spin down cold partitions. Show why this hybrid model cuts storage bills and still supports 95 percentile query SLAs.

12. Why should related tables have explicit foreign-key constraints instead of bare BIGINT fields, and when would you employ ON DELETE CASCADE vs. SET NULL?

    Explain that FKs enforce referential integrity, enable better join-planner statistics, and prevent orphaned rows. Use CASCADE when child rows are meaningless without the parent (e.g., order_items after order deletion) and SET NULL when the child can outlive the parent but should lose the reference (e.g., a nullable manager_id after a manager leaves).

Behavioral & Culture-Fit Questions

Your ability to thrive in Optiver’s “own-it” culture is assessed through Optiver behavioral interview questions. Interviewers seek examples of when you took ownership of a critical production incident, iterated rapidly under tight deadlines, or partnered with quants to deliver a high-impact feature. Emphasize clear communication, data-driven decision-making, and how you learned from failures to prevent future issues.

1. Describe a software project where you had to push through major obstacles—what went wrong and how did you get the release over the finish line?

   Optiver’s trading stack tolerates almost no downtime, so interviewers want to see how you debug under fire. Highlight a moment when prod-blocking bugs, vendor library quirks, or hard latency ceilings threatened delivery. Walk through how you triaged logs, built minimal repros, or rewrote hot paths in Cython, and—crucially—how you kept stakeholders calm with crisp status updates. Quantify the payoff (e.g., “cut GC pauses by 70 µs and shipped on schedule”).

2. What techniques have you used to make complex technical data or metrics understandable to non-engineers—especially traders or risk managers?

   Great answers translate packet-capture traces or p99 latency histograms into plain-language impacts like “order book refresh now arrives 3 ms sooner, saving X bps in slippage.” Mention live Grafana boards, one-page runbooks, or lunchtime demos that demystified the engine for ops, compliance, or trading staff and led to faster incident resolution.

3. If your current manager were in the room, what strengths would they praise and which growth areas would they flag?

   Tie strengths to Optiver’s culture—e.g., “obsessive micro-benchmarking” or “writing deterministic tests for multi-threaded code.” For weaknesses, pick something real yet improving (perhaps over-engineering before clarifying P&L impact) and show the concrete steps you’re taking—like pairing with quants to define “good-enough” solutions earlier.

4. Recall a time you struggled to get buy-in from traders or product owners on a technical change—how did you bridge the gap?

   Outline the initial disconnect, the stakes (latency budgets, risk limits), and how you used data—A/B fills, replay simulations—to bring skeptics on board. Emphasize empathy for P&L perspectives and the habit of distilling options into simple trade-offs rather than deep-dive jargon.

5. Why do you want to engineer software at Optiver rather than another trading firm or a big-tech company?

   Show that you’ve researched their culture of ownership, ultra-low-latency challenges, and direct line of sight between code and trading performance. Explain how the firm’s feedback loops (deploy → trade → measure) match your desire for rapid iteration and measurable impact.

6. When several high-priority fixes and feature asks arrive simultaneously, how do you sequence the work and keep yourself organised?

   Discuss ranking by P&L risk, regulatory deadlines, and engineering complexity. Mention concrete tactics—Kanban swim-lanes for “hot-fix,” “next session,” and “later,” or reserving focus blocks for deep optimisation while still triaging alerts. Stress the habit of over-communicating ETAs so trading desks can adjust.

7. Tell us about a time you had to optimise an algorithm by orders of magnitude—what profiling steps did you follow and how did you confirm real-world improvement?

   This probes your scientific approach to performance work: hypothesis → micro-benchmarks → system-wide metrics. Show how you verified gains in a production-like environment (e.g., replayed market data) rather than relying solely on synthetic tests.

8. Give an example of how you kept your codebase resilient to rare, high-impact edge cases—what defensive design or chaos-testing techniques did you use?

   Optiver cares deeply about tail-risk. Describe fuzz testing protocols, circuit-breaker patterns, or failure-injection runs that surfaced hidden race conditions before they could trigger an exchange disconnect or fat-finger trade.

Online Assessment Breakdown

Before the live rounds, you’ll complete an Optiver coding assessment or Optiver online test—typically a timed HackerRank challenge. This screen covers algorithm puzzles, SQL queries on market snapshots, and Python exercises for data munging. Familiarize yourself with the platform’s interface, review sample-question articles, and simulate the test environment to avoid surprises on test day.

Getting ready for Optiver’s rigorous loop means targeted practice and a mindset of continuous refinement. Below are focused strategies to elevate your performance at each stage.

Simulate the OA with Timed HackerRank Sets

Recreate the exact conditions of the Optiver hackerrank by time-boxing full practice tests. Track your speed and accuracy, then analyze mistakes to adjust your pacing and approach.

Drill Low-Latency Data Structures

Deeply understand queues, ring buffers, lock-free stacks, and other constructs critical for high-throughput systems. Use the context of Optiver - Campus Software Engineer Test 2025 - AMS scenarios to ground your practice in realistic trading-floor demands.

Practice System-Design Drills

Regularly sketch and verbally walk through designs for data pipelines, service meshes, and caching layers. Reference the patterns expected in the Optiver system design interview to sharpen your ability to justify architectural decisions under time constraints.

Mock Interviews & Feedback Loops

Schedule full mock loops that mirror the real process and solicit detailed critique on both solutions and communication. Label these rehearsals as Optiver SWE interview simulations to keep your focus on the specific culture and expectations.

Remember: start with a brute-force solution to validate correctness, then iteratively optimize for performance. At every step, verbalize your trade-offs so interviewers can follow your reasoning and appreciate your design choices.

What Is the Average Salary for an Optiver Software Engineer?

Optiver software engineer salary bands are highly competitive, reflecting both base pay and performance bonuses. On average, the Optiver SWE salary places you in the top quartile of the industry, while software engineer Optiver salary often includes significant equity components. For senior roles, the Optiver software engineer salary range scales further with experience and impact.

How Does the Online Assessment Differ for Interns vs Full-Time?

The Optiver software engineer intern interview typically focuses on core programming problems and foundational data-structure questions. In contrast, the full-time track mirrors production complexity, with additional scenario-based puzzles. Expect a lighter load of system-design prompts in the Optiver software engineer intern loop.

Are There Practice Questions for the Optiver OA?

Yes—our library includes Optiver online assessment sample questions hackerrank that simulate the exact format and difficulty of the live test. These cover SQL joins, algorithmic challenges, and real-time data-processing scenarios.

Does Optiver Use Levels.fyi Titles?

Optiver’s internal grading, or Optiver levels, aligns loosely with public benchmarks, but the firm emphasizes role impact over title. While you can refer to Optiver levels.fyi for general guidance, the hiring team evaluates based on your demonstrated skills and contributions.

What’s the Interview Timeline for Graduates?

The Optiver graduate software engineer path usually spans 3–4 weeks from application to offer. Preparation for the Optiver graduate software developer online assessment sample questions is crucial, as campus candidates face a dedicated, speed-focused coding screen early in the process.

Cracking the Optiver data-feed challenges and low-latency coding puzzles starts with rehearsing the online assessment, honing your system-design trade-offs, and practicing clear, concise communication. To put these strategies into action, try a mock interview with Interview Query or dive into our full Optiver interview questions pillar for insights across all roles.

Good luck on your journey—engineer your success like Jeffrey Li, who leveraged focused prep to join Optiver’s top engineering ranks: Jeffrey Li’s Story.