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Olh Software Engineer Interview Guide

Olh Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Olh, you play a vital role in building, maintaining, and scaling complex technical solutions that drive critical engineering and scientific initiatives. You will contribute directly to high-impact software systems, collaborating closely with multidisciplinary teams across major research and operational facilities. Your work empowers stakeholders to process complex data, streamline workflows, and ensure the reliability and safety of large-scale technical infrastructure. This role requires a unique balance of rigorous software development principles and an appreciation for domain-specific engineering challenges. Whether you are developing robust database solutions, crafting intuitive design applications, or optimizing backend architectures, your code directly influences project outcomes and operational efficiency.

This guide is scoped to a Software Engineer candidate at Olh.

Focus areas

  • Software Engineering
  • Software Development
  • Design Engineering

The Olh interview process

Olh candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screening

Initial discussion with a recruiter to review your background and assess role fit.

What to demonstrate

  • Initial discussion with a recruiter to review your background and assess role fit
  • Depth in Software Engineering

How to prepare

  • Be able to walk your CV end to end in two minutes, and say why this company specifically.
  • Have your salary expectations, notice period and location constraints ready, and ask for the rest of the loop in writing.

Technical Assessments

Evaluation of your technical skills through coding challenges and problem-solving exercises.

What to demonstrate

  • Evaluation of your technical skills through coding challenges and problem-solving exercises
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: Explain the process of normalizing a database and when you might choose to denormalize for performance.
  • Answer aloud and timed: How do you handle database migrations and schema updates in a production environment without downtime?

Panel Interviews

Comprehensive interviews with multiple team members to assess technical depth and cultural alignment.

What to demonstrate

  • Comprehensive interviews with multiple team members to assess technical depth and cultural alignment
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: What strategies do you use for debugging performance bottlenecks in a multi-tiered application?
  • Answer aloud and timed: Describe your experience with writing clean, maintainable, and thoroughly tested code in languages like Python or Java.

Final Leadership Discussions

Conversations with leadership to evaluate overall fit and alignment with company values.

What to demonstrate

  • Conversations with leadership to evaluate overall fit and alignment with company values
  • Depth in Software Engineering

How to prepare

  • Answer aloud and timed: These questions evaluate your ability to architect scalable, modular, and maintainable software systems from scratch.
  • Answer aloud and timed: How do you approach designing a scalable backend service to handle high-frequency data ingestion?

Where candidates lose points

Master the fundamentals

Brush up on core computer science concepts, data structures, and database optimization techniques, as these form the baseline of technical evaluations.

Practice articulating trade-offs

When answering system design questions, always discuss the pros and cons of your chosen technologies, considering scalability, maintainability, and constraints.

Structure your behavioral responses

Use structured storytelling frameworks to highlight your problem-solving process, leadership impact, and how you handle project ambiguity.

Ask insightful questions

Prepare thoughtful questions for your interviewers about their tech stack, engineering challenges, and team culture to demonstrate genuine interest.

Focus on code clarity

During live coding exercises, prioritize writing readable, modular code over clever shortcuts, and communicate your debugging steps clearly.

Questions & practice

How do you prioritize features and bug fixes when facing tight project deadlines and competing requirements?

Medium · Problem Solving and Engineering Methodol

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

  1. Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
  2. Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
  3. For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
  4. Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

  • The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
  • Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach

  1. Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
  2. Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
  3. Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
  4. Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.

Follow-up

  • The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
  • The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

How do you optimize complex SQL queries for large datasets with millions of records?

Medium · Technical and Database Development

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. Handle the rows that do not match: that is usually the actual question.

Follow-up

  • How does the query change if that join becomes one-to-many?
  • What happens to this when the table is ten times larger?

Explain the process of normalizing a database and when you might choose to denormalize for performance.

Medium · Technical and Database Development

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. Handle the rows that do not match: that is usually the actual question.

Follow-up

  • How does the query change if that join becomes one-to-many?
  • What happens to this when the table is ten times larger?

This category tests your proficiency in core programming languages, database design, and data retrieval method

Medium · Technical and Database Development

This category tests your proficiency in core programming languages, database design, and data retrieval methods.

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

These questions evaluate your ability to architect scalable, modular, and maintainable software systems from s

Medium · Software Design and Architecture

These questions evaluate your ability to architect scalable, modular, and maintainable software systems from scratch.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How do you approach designing a scalable backend service to handle high-frequency data ingestion?

Medium · Software Design and Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

What design patterns do you frequently rely on, and why are they effective for decoupled architectures?

Medium · Software Design and Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How do you ensure high availability and fault tolerance in critical distributed systems?

Medium · Software Design and Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

Walk through your process for documenting software architecture and communicating technical decisions to stake

Medium · Software Design and Architecture

Walk through your process for documenting software architecture and communicating technical decisions to stakeholders.

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How do you manage technical debt while maintaining rapid delivery cycles?

Medium · Software Design and Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How do you approach testing strategies, including unit, integration, and end-to-end testing?

Medium · Problem Solving and Engineering Methodol

Approach

  1. Say who the caller is and what they do when the call fails halfway.
  2. Define the identity of a request so a retry cannot double-apply it.
  3. Separate accepted, pending, failed and confirmed; they are different facts.
  4. Design the error taxonomy before the success shape; callers branch on it.

Follow-up

  • What happens if the caller retries after a timeout?
  • How does a client discover it is on an old version of this contract?

What strategies do you use for debugging performance bottlenecks in a multi-tiered application?

Medium · Technical and Database Development

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

This area focuses on how you approach ambiguous technical challenges, troubleshoot failures, and collaborate w

Medium · Problem Solving and Engineering Methodol

This area focuses on how you approach ambiguous technical challenges, troubleshoot failures, and collaborate with engineering peers.

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Olh candidates report.

Day 1 — Map the Olh loop

  • Write out the reported sequence: Recruiter Screening, Technical Assessments, Panel Interviews, Final Leadership Discussions.
  • For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 4 reported rounds, with the weakest marked.

Day 2 — Work Software Engineering

  • Spend the session on Software Engineering, which Olh candidates report being tested on.
  • Write one worked example in Software Engineering and time yourself on it.

Deliverable: One timed worked example in Software Engineering.

Day 3 — Work Software Development

  • Spend the session on Software Development, which Olh candidates report being tested on.
  • Write one worked example in Software Development and time yourself on it.

Deliverable: One timed worked example in Software Development.

Day 4 — Work Design Engineering

  • Spend the session on Design Engineering, which Olh candidates report being tested on.
  • Write one worked example in Design Engineering and time yourself on it.

Deliverable: One timed worked example in Design Engineering.

Day 5 — Answer out loud: Technical and Database Development

  • Answer aloud, timed: This category tests your proficiency in core programming languages, database design, and data retrieval methods.
  • Answer aloud, timed: How do you optimize complex SQL queries for large datasets with millions of records?

Deliverable: Spoken answers to 2 reported Technical and Database Development question(s), under time.

Day 6 — Answer out loud: Software Design and Architecture

  • Answer aloud, timed: These questions evaluate your ability to architect scalable, modular, and maintainable software systems from scratch.
  • Answer aloud, timed: How do you approach designing a scalable backend service to handle high-frequency data ingestion?

Deliverable: Spoken answers to 2 reported Software Design and Architecture question(s), under time.

Day 7 — Answer out loud: Problem Solving and Engineering Methodology

  • Answer aloud, timed: This area focuses on how you approach ambiguous technical challenges, troubleshoot failures, and collaborate with engineering peers.
  • Answer aloud, timed: Describe a time when you encountered a critical production bug; how did you isolate and resolve it?

Deliverable: Spoken answers to 2 reported Problem Solving and Engineering Methodology question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

How do you handle database migrations and schema updates in a production environment without downtime?

Medium · Technical and Database Development

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Describe your experience with writing clean, maintainable, and thoroughly tested code in languages like Python

Medium · Technical and Database Development

Describe your experience with writing clean, maintainable, and thoroughly tested code in languages like Python or Java.

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Describe a time when you encountered a critical production bug; how did you isolate and resolve it?

Medium · Problem Solving and Engineering Methodol

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Explain how you conduct code reviews and what elements you prioritize to maintain high code quality standards.

Medium · Problem Solving and Engineering Methodol

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Tell me about a time you had to learn a new technology or domain rapidly to deliver a project.

Medium · Problem Solving and Engineering Methodol

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?