Optiver Data Engineer Interview Guide (2025) – Process, Questions, and Prep

Optiver is a leading market-making firm where speed, precision, and innovation drive success. As a data engineer on our team, you’ll build the infrastructure that powers real-time trading decisions and ensures our global operations run flawlessly. In this guide, we’ll walk through what makes the Optiver data engineer role unique and how to prepare for the interview process ahead.

Role Overview & Culture

The Optiver data engineer role centers on ingesting high-frequency market feeds, designing low-latency ETL pipelines, and maintaining end-to-end data integrity for quantitative researchers and traders. On a typical day, you’ll collaborate closely with quants to optimize data schemas, implement streaming analytics with Kafka and Flink, and automate reliability checks to catch anomalies before they impact P&L. Optiver’s “own-it” culture empowers engineers to propose and execute improvements autonomously, fostering a flat structure where ideas are judged on merit, not hierarchy.

Why This Role at Optiver?

In this position, your work directly influences trading performance and P&L, giving you immediate visibility into the impact of your solutions. You’ll leverage a modern stack—Kafka for real-time messaging, Flink for stream processing, and Rust or Python for core services—to innovate at scale. Optiver offers top-tier compensation and a fast track for career growth, enabling you to take on increasing responsibility as you demonstrate expertise. Ready to dive in? Next, we’ll explore the Optiver data engineer interview process and the types of questions you can expect.

Before diving into question formats, it’s helpful to understand the stages you’ll navigate when interviewing for a data engineer position at Optiver. From initial screening to on-site loops, each step evaluates your technical skills, problem-solving approach, and cultural fit within our fast-paced trading environment.

Application & Recruiter Screen

In this first stage, a recruiter reviews your résumé for relevant experience in real-time data systems and pipelines. You’ll discuss your motivation for joining Optiver, clarify your background with technologies like Kafka or Flink, and get an overview of the process timeline. This call sets expectations for the subsequent technical rounds.

Take-Home SQL/Python Assignment

Next, you’ll receive a take-home project—often involving complex SQL queries or a Python script to process simulated market data. This assignment tests your ability to write clean, efficient code under real-world constraints, handling edge cases and communicating assumptions in your submission.

Technical Deep-Dive & Live Coding

Candidates who pass the take-home are invited to a live session where you’ll pair with an engineer on coding exercises and deeper discussions around your take-home solution. Expect whiteboard-style algorithm problems, debugging tasks, and optimizations for performance and scalability in Python or Rust.

On-Site System Design + Behavioral Loop

During the on-site loop, you’ll sketch end-to-end data architectures for use cases like low-latency event streaming or backfill pipelines. Each of these design interviews is followed by behavioral conversations exploring Optiver’s “own-it” culture—how you proactively identify issues, collaborate under pressure, and deliver robust solutions.

Offer & Team Match

After all interviews are complete, the hiring committee consolidates feedback and determines fit. If successful, you’ll receive an offer and discuss team alignment, ensuring your role leverages your strengths and interests. Throughout, Optiver maintains rapid feedback cycles, often concluding within a week of your on-site visits.

When preparing for an Optiver data engineer role, you’ll face a mix of technical, architectural, and behavioral discussions designed to mirror the challenges of powering ultra-low-latency trading systems. Below is an overview of the types of questions you can expect—no need to worry about specific prompts here, just focus on understanding each category.

Coding / Technical Questions

Optiver data engineer candidates should be ready for SQL window-function tasks that calculate running aggregates on tick data, streaming join optimization scenarios to merge market feeds with reference tables at scale, and Python data-munging exercises that clean and reshape real-time order book events. These questions evaluate your ability to write efficient, production-quality code under tight performance constraints, handle edge cases in live data, and communicate your assumptions and trade-offs clearly.

1. Write find_iqr(nums) to compute the inter-quartile distance of an unsorted numeric array.

   Sort the list, locate the first (Q1) and third (Q3) quartile positions using the Tukey method or linear interpolation, and return Q3 − Q1. A correct solution handles odd vs. even length arrays, uses 0-based indices carefully, and runs in O(n log n) due to the sort (or O(n) with a median-of-medians selection). Mentioning how outliers do not influence the IQR and why it’s preferred over range in exploratory analysis shows statistical depth.

2. Calculate first-touch attribution for every user who converted.

   Join attribution to user_sessions, filter conversion = TRUE, then apply ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY session_ts) and keep row_number = 1. Selecting the corresponding channel gives the discovery source that eventually led to purchase. Good answers discuss composite indexes on (user_id, session_ts) and how to treat users with multiple same-timestamp sessions.

3. Implement rearranging_digits(n) that returns the smallest number larger than n achievable by re-ordering its digits, or None if impossible.

   Use the “next permutation” algorithm: scan from right to left to find the first digit that is smaller than a digit to its right, swap it with the next-larger digit found to its right, then reverse the suffix. Complexity is O(k) for k digits and works in-place. Edge cases—descending input (return None) and repeated digits—must be covered.

4. Write decreasing_values(arr) to remove any element that has a smaller value to its right.

   Traverse from right-to-left, keeping a running maximum; prepend elements that are ≥ current max to an output list, then reverse. This single-pass solution is O(n) time, O(n) space, and avoids nested scans that would degrade to O(n²).

5. Generate a normal distribution truncated at a given percentile.

   Accept percentile_threshold, m, and sd; compute the cutoff value via the inverse CDF, then repeatedly sample from N(m, sd) until the sample ≥ cutoff (or ≤ cutoff for left truncation). Vectorized NumPy sampling plus rejection keeps code concise; mentioning efficiency trade-offs or inverse-transform sampling demonstrates understanding of distribution tailoring.

6. Merge N pre-sorted integer lists into one sorted list without using built-ins.

   Maintain a min-heap of the first element from each list; pop the smallest, append to output, then push the next element from that list until all lists are exhausted. The heap guarantees O(N log k) time where N is total elements and k is number of lists, with O(k) extra space.

7. Select the 2nd-highest salary within Engineering.

   Apply DENSE_RANK() OVER (ORDER BY salary DESC) on rows WHERE department='Engineering' and filter for rank = 2; a correlated sub-query (SELECT MAX(salary) FROM … WHERE salary < (SELECT MAX …)) also works. Be explicit about handling ties at the top and NULL safeguards.

8. Return the last transaction of each calendar day.

   Partition by DATE(created_at) and order by created_at DESC, then keep ROW_NUMBER() = 1. Output id, created_at, transaction_value; finally, ORDER BY created_at. Casting timestamps to dates (or using DATE_TRUNC) ensures correct grouping across time zones.

9. Implement rectangle_overlap(a, b) to detect whether two axis-aligned rectangles overlap.

   First normalize each rectangle to (min_x, max_x, min_y, max_y). Overlap exists unless one is strictly left/right or above/below the other, so return not (r1.max_x < r2.min_x or r2.max_x < r1.min_x or r1.max_y < r2.min_y or r2.max_y < r1.min_y). Include corner-touch and edge-touch as overlap per the prompt.

10. Get the top 3 earners per department (fewer if department size < 3).

    Join employees with departments, compute DENSE_RANK() OVER (PARTITION BY dept_id ORDER BY salary DESC), filter for ranks ≤ 3, then sort by department_name ASC, salary DESC. This window-function pattern powers leaderboard-style dashboards and scales with index (dept_id, salary DESC).

System / Product Design Questions

Expect to architect fault-tolerant market-data pipelines that can survive node failures and network partitions, devise schema-evolution strategies that allow seamless column additions or type changes without data loss, and propose real-time monitoring frameworks with alerting thresholds for latency or data drift. These discussions gauge your end-to-end design skills, from ingestion through processing to serving, as well as your understanding of operational concerns in a trading context.

1. Why explicitly declare foreign-key constraints instead of storing raw BIGINTs? When should you use ON DELETE CASCADE versus SET NULL?

   Foreign-key constraints let the database engine enforce referential integrity, prevent orphaned rows, and enable cost-based optimizers to choose better join strategies; a plain BIGINT sidesteps those guarantees and shifts data-quality risk onto every writer and analyst. Use ON DELETE CASCADE when child records are meaningless without the parent (e.g., order → order-items) and you’re sure mass deletions won’t surprise downstream systems; choose SET NULL when child rows can stand alone but should lose the parent link (e.g., optional manager_id on an employees table). Mention that both options trigger more I/O than simple deletes, so bulk purges may need batching or deferred constraints.

2. Design an international e-commerce warehouse pipeline and reporting stack. What factors and clarifying questions come first?

   Start by asking about SKU-volume forecasts, regional latency targets, currency / tax rules, GDPR data-residency, and real-time vs. batch freshness for the vendor portal. A common architecture is: (1) landing S3/Blob buckets per region; (2) stream ingest (Kafka → Flink) for inventory deltas; (3) Snowflake/BigQuery with separate data-sharing zones for hourly, daily, and audit-level history; (4) dbt-orchestrated ELT to build fact tables (orders, returns, inventory) materialized with incremental models; (5) Looker or Tableau extracts for daily/weekly reports, plus a vendor-facing REST/GraphQL API reading from a read-only replica. Partitioning by warehouse_id and date plus column-level encryption meets both performance and compliance.

3. Schema + analytics for tracking Golden Gate bridge crossing times.

   Schema: crossings(id, plate, model, entry_ts, exit_ts), with a generated column duration_sec. Fastest today: use WHERE entry_ts::date = CURRENT_DATE and ORDER BY duration_sec ASC LIMIT 1. Model with best average: aggregate by model, compute AVG(duration_sec), and order ascending, again filtered to today’s date. Indexing on (entry_ts) accelerates daily slices; a partial index on today’s rows keeps writes cheap while supporting real-time leaderboards.

4. Design a stand-alone fast-food restaurant database, then write two analytical queries.

   Core tables: orders(order_id, order_ts, customer_id), order_items(order_id, item_id, qty, price), menu_items(item_id, category, name), plus a small customers table for loyalty. Top-3 revenue items yesterday: sum qty × price per item_id for order_ts BETWEEN yesterday’s boundaries, order by revenue DESC limit 3. Drink attach-rate: divide count of orders containing a drink by total orders yesterday. A materialized daily sales fact keeps POS reporting snappy.

5. Build an hourly → daily → weekly active-user pipeline from a data lake.

   Land raw events in partitioned object storage (e.g., s3://lake/app_events/date=YYYY-MM-DD/hour=HH). A Spark/Glue job every hour reads the last hour’s partition, writes an incremental hourly_active_users table keyed by (user_id, event_hour). Downstream dbt models roll up distinct users into daily_active_users and weekly_active_users incremental snapshots. Airflow orchestrates DAG dependencies so the dashboard query hits pre-aggregated tables and refreshes within minutes of the hour.

6. Cost-efficient two-year click-stream storage for ≈ 600 M events/day (Kafka → analytics).

   Persist Kafka topics to a compressed columnar format (Iceberg/Delta) in object storage with hourly partitions and ZSTD or Snappy compression—cheap and immutable. Expose analytics through Athena/Presto for ad-hoc and a Redshift/Snowflake “hot” layer that retains the most recent 90 days for BI dashboards. Lifecycle rules down-tier older data to infrequent-access storage; Iceberg’s hidden partition pruning keeps queries on two-year spans affordable.

7. Diagnosing slow OLAP inventory roll-ups & choosing an aggregation strategy.

   Monthly/quarterly reports crawl when fact tables aren’t partitioned by snapshot_date or when every query scans raw transaction-level rows. Implement aggregate tables/cube materialization (e.g., pre-compute SUM(qty) by sku, month) and use incremental refresh so only the latest partitions recompute. Bitmap or HyperLogLog sketches can accelerate distinct-count KPIs. Note assumptions: Snowflake virtual-warehouse, billions of rows, strict SLA for CFO close.

8. Design a machine-learning system that reduces missing/wrong orders.

   Data sources: item-level order history, restaurant prep accuracy, driver GPS, customer complaint tickets, photo-based bag-scan. Offline layer: feature store built in Feast, model (gradient-boosted trees or deep tabular net) predicts probability of an error before pickup. Real-time layer: scoring service in Kubernetes injected into dispatch flow; high-risk orders trigger double-confirmation UI or automatic credit pre-authorization. Monitor precision/recall and run shadow A/B until the false-positive cost (added latency) is outweighed by refund savings.

Behavioral or Culture-Fit Questions

Optiver’s “own-it” culture means you’ll be asked to share ownership stories where you identified and fixed production outages, handled post-mortems under pressure, and collaborated closely with quantitative researchers or traders. Interviewers look for evidence of proactive communication, a sense of urgency balanced with sound engineering judgment, and the ability to thrive in a high-stakes, fast-moving environment.

1. Describe a data project you worked on. What were some of the challenges you faced?

   Pick a project that maps to Optiver’s scale-and-speed culture—e.g., migrating a terabyte-per-hour Kafka stream into a tick-level warehouse or building a fault-tolerant CDC pipeline for market data. Outline the architectural goals, then zoom in on two or three thorny issues: back-pressure during exchange outages, schema drift from upstream publishers, or millisecond-level SLAs that clashed with batch tooling.

2. What are some effective ways to make data more accessible to non-technical people?

   Frame accessibility around speed and correctness: explain how you’ve exposed raw trading data via governed views, built Looker/Power BI semantic layers with pre-aggregated measures (VWAP, liquidity buckets), and created self-service notebooks that hide Spark minutiae behind parameter widgets. Discuss data-quality contracts—automated validation tests with color-coded dashboards—so risk managers can trust the figures without SQL deep dives.

3. How would you answer when asked about your greatest strengths and weaknesses?

   Tie strengths to Optiver’s edge: say you’re relentless about latency profiling, can trace a microburst from switch counters to JVM garbage collections, and thrive on shipping fully tested code before market open. For weaknesses, choose an honest but non-fatal trait—e.g., over-engineering monitoring alerts—and show the concrete steps you’ve taken (alert budgets, peer reviews) to rein it in. Finish by noting that your manager would describe you as “the person who leaves dashboards cleaner than they found them, but sometimes needs a reminder to hit done rather than perfect.”

4. Talk about a time when you had trouble communicating with stakeholders. How did you overcome it?

   Optiver engineers sync with traders who need answers now. Describe a specific incident—perhaps latency numbers were quoted in mean instead of P99 and the desk panicked. Explain how you re-framed the metric in trading terms, produced an annotated chart during the same session, and instituted a shared glossary to prevent future mix-ups. Highlight empathy, real-time clarity, and the follow-through of updating runbooks.

5. Why did you apply to our company, and what makes you a strong fit?

   Show you’ve done homework: Optiver’s success hinges on ultra-low-latency systems and a feedback loop between research and production. Mention your passion for squeezing microseconds out of codec choices, your track record of automating data-quality gates for real-time decision engines, and your excitement about collaborating with quant researchers who iterate daily. Tie your career goals—owning critical data paths that move P&L—to Optiver’s engineering autonomy and culture of continuous improvement.

6. How do you prioritize multiple deadlines, and how do you stay organized?

   Describe a triage framework aligned with trading impact: first classify requests by risk (reg-reporting > trading analytics > ad-hoc), then by latency savings or cost of delay. Explain how you block focus time for deep work (kernel-bypass tuning) while using Kanban swim-lanes for reactive tasks like exchange-feed fixes. Mention on-call rotations, run-books, and metrics-driven retros to ensure nothing slips between shifts.

7. Describe a moment when you shipped code that later caused an incident in production. What happened, and how did you prevent it from recurring?

   This probes ownership, root-cause analysis skills, and your commitment to a blameless culture—critical when milliseconds can cost money.

8. Give an example of how you balanced ultra-low-latency requirements with data integrity or fault-tolerance needs. What trade-offs did you make and why?

   Optiver engineers regularly decide between raw speed and safety nets; interviewers want to see your decision-making framework and risk awareness.

Diving into these interviews with targeted preparation will help you showcase both your technical chops and cultural alignment. Focus on the following areas to build confidence and clarity.

Study the Role & Culture

Before tackling any coding or design questions, revisit Optiver’s core values of speed, ownership, and autonomy. Understand how data engineers drive real-time decision-making for trading desks and be ready to speak to examples where you’ve “owned” a complex pipeline or algorithm under tight deadlines.

Practice Common Question Types

Allocate roughly 40% of your prep time to SQL and Python coding challenges, 30% to streaming design scenarios, and 30% to behavioral reflections. This balanced approach mirrors the interview weighting and ensures you’re well-rounded across all areas.

Think Out Loud & Ask Clarifying Questions

Interviewers want to see your trade-off reasoning in action. When given a prompt, vocalize your assumptions, ask questions to narrow scope, and iterate on your approach. This collaboration style is critical in Optiver’s team-oriented, high-velocity environment.

Brute Force, Then Optimize

Start your solutions with a correct but naïve batch ETL or join approach, then progressively refine for streaming, latency, and resource efficiency. Demonstrating this iterative mindset shows you can deliver working solutions quickly and improve them under time pressure.

Mock Interviews & Feedback

Simulate the real loop by pairing with engineers who understand trading-firm demands. Conduct full mock sessions covering coding, design, and behavioral rounds, then incorporate feedback to sharpen your delivery and technical depth.

Mastering streaming design, low-latency coding, and clear communication is your path to success in the Optiver data engineer interview. Next steps:

• Book a mock interview
• Check out Interview Query’s DE learning path
• Subscribe for exclusive data-engineer content.
• Explore other guides, tap into the DE learning path, and draw inspiration from success stories like Hanna Lee’s.

Good luck!