As a Software Engineer at Imply, you are at the forefront of the big data revolution. You will be instrumental in building and scaling Imply Lumi, the industry’s first observability warehouse, and contributing to the ecosystem surrounding Apache Druid. This role is not just about writing code; it is about solving complex challenges related to high-performance query execution, data ingestion, and system reliability at a massive scale. Your work will directly impact how major organizations—such as Pepsi, Reddit, Roblox, and Salesforce—manage their observability and security data. Because Imply is founded by the creators of Apache Druid, you will be working within a culture of deep technical expertise where performance, cost-efficiency, and user-centric design are paramount. You will be expected to bridge the gap between complex backend infrastructure and intuitive user-facing services, helping to redefine the future of data analytics.
Phone Screen
reportedInitial call with a recruiter to discuss your background.
What to demonstrate
- Initial call with a recruiter to discuss your background
- Depth in Apache Druid / OLAP
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
reportedA series of technical assessments including coding exercises and system design discussions.
What to demonstrate
- A series of technical assessments including coding exercises and system design discussions
- Depth in Apache Druid / OLAP
How to prepare
- Answer aloud and timed: Explain your experience with technical writing and developer-oriented documentation.
- Answer aloud and timed: What are React hooks and why are they useful?
Coding Exercises
reportedPractical coding exercises, which may include a take-home challenge.
What to demonstrate
- Practical coding exercises, which may include a take-home challenge
- Depth in Apache Druid / OLAP
How to prepare
- Answer aloud and timed: How would you design a system to find the top N users given a large log file?
- Answer aloud and timed: Explain your approach to implementing list virtualization on the front end.
System Design Discussion
reportedDeep-dive discussions on system design with engineers and hiring managers.
What to demonstrate
- Deep-dive discussions on system design with engineers and hiring managers
- Depth in Apache Druid / OLAP
How to prepare
- Answer aloud and timed: Discuss how you would handle data source creation and UI flow improvements.
- Answer aloud and timed: Walk through a system design scenario similar to the challenges Imply solves regarding data scale and query performance.
PracHub editorial advice for the preparation topics above.
Prioritize the "Why
In every technical or design round, be prepared to explain the rationale behind your decisions. Don't just provide a solution; explain the trade-offs you considered.
Be Transparent
If you are unsure about a specific technology, be honest. Interviewers at Imply value intellectual honesty and a willingness to learn over pretending to know everything.
Review Your Resume
Be prepared to discuss every project on your resume in detail. You may be asked to walk through the challenges you faced and how you overcame them.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Spot and correct issues in a provided code sample.
Spot and correct issues in a provided code sample.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Explain your thinking process while working through a time-bound technical exercise.
Explain your thinking process while working through a time-bound technical exercise.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
Collapse a redelivered event batch into per-aggregate high-water marks
You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.
Approach
- One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
- Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
- Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
- If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.
Follow-up
- The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
- How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?
Find version gaps and relay lag with window functions
outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.
Approach
- Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
- Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
- Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
- Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.
Follow-up
- Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
- Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?
Replace offset paging on the resource feed with keyset
resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.
Approach
- Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
- Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
- Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
- Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.
Follow-up
- The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
- Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?
What are React hooks and why are they useful?
What are React hooks and why are they useful?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 would you design a system to find the top N users given a large log file?
How would you design a system to find the top N users given a large log file?
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Explain your approach to implementing list virtualization on the front end.
Explain your approach to implementing list virtualization on the front end.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Discuss how you would handle data source creation and UI flow improvements.
Discuss how you would handle data source creation and UI flow improvements.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 a system design scenario similar to the challenges Imply solves regarding data scale and query pe
Walk through a system design scenario similar to the challenges Imply solves regarding data scale and query performance.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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?
Implement a search function for files on a distributed file system.
Implement a search function for files on a distributed file system.
Approach
- Fix the scope first: who calls this, how often, and what they do when it fails.
- Name the read and write paths separately; they rarely have the same bottleneck.
- Choose a partition key and say what query it makes expensive.
- 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 to troubleshoot if Kafka offsets are missing at the consumer?
How to troubleshoot if Kafka offsets are missing at the consumer?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- 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?
How to troubleshoot if Kubernetes pods are randomly going offline?
How to troubleshoot if Kubernetes pods are randomly going offline?
Approach
- Establish what changed and when, before forming any theory.
- Pick a bisection that eliminates candidates whichever way it turns out.
- Check the instrumentation before believing the symptom.
- 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?
Built from the rounds and topics Imply candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Imply loop
- Write out the reported sequence: Phone Screen, Technical Assessments, Coding Exercises, System Design Discussion.
- 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.
02Work Apache Druid / OLAP
- Spend the session on Apache Druid / OLAP, which Imply candidates report being tested on.
- Write one worked example in Apache Druid / OLAP and time yourself on it.
Deliverable: One timed worked example in Apache Druid / OLAP.
03Work SQL
- Spend the session on SQL, which Imply candidates report being tested on.
- Write one worked example in SQL and time yourself on it.
Deliverable: One timed worked example in SQL.
04Work System Design
- Spend the session on System Design, which Imply candidates report being tested on.
- Write one worked example in System Design and time yourself on it.
Deliverable: One timed worked example in System Design.
05Answer out loud: Technical Domain & Infrastructure
- Answer aloud, timed: How to troubleshoot if Kafka offsets are missing at the consumer?
- Answer aloud, timed: How to troubleshoot if Kubernetes pods are randomly going offline?
Deliverable: Spoken answers to 2 reported Technical Domain & Infrastructure question(s), under time.
06Answer out loud: System Design & Architecture
- Answer aloud, timed: How would you design a system to find the top N users given a large log file?
- Answer aloud, timed: Explain your approach to implementing list virtualization on the front end.
Deliverable: Spoken answers to 2 reported System Design & Architecture question(s), under time.
07Answer out loud: Coding & Problem Solving
- Answer aloud, timed: Implement a search function for files on a distributed file system.
- Answer aloud, timed: Spot and correct issues in a provided code sample.
Deliverable: Spoken answers to 2 reported Coding & Problem Solving question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Explain your experience with technical writing and developer-oriented documentation.
Explain your experience with technical writing and developer-oriented documentation.
Approach
- Pick a story where you made the decision, not one where you watched it.
- State the situation in two sentences and spend the rest on the reasoning.
- Give the blast radius: what could have broken, and what you measured.
- 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?
Estimate work you have never done and defend the range
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
Approach
- Decompose into independently deployable steps before estimating anything: add the column nullable, write both shapes, backfill in batches, verify, move reads, stop writing the old shape, drop it. That is four deploys spread over days, and the calendar estimate is dominated by them rather than by the loop's runtime.
- Do the arithmetic aloud for the part that has arithmetic in it: batch size times number of batches times per-batch duration, at a write rate the primary can absorb alongside roughly 1.2k writes per second of production traffic. The loop is throttled by replication lag and lock waits, not by how fast it can issue statements.
- Price the schema step by its lock rather than its statement duration. In PostgreSQL an ALTER TABLE taking ACCESS EXCLUSIVE waits for every open transaction on that table while later queries queue behind it, so a millisecond change issued during a thirty-second analytics query stalls that table for thirty seconds. Adding a nullable column with a non-volatile default avoids a rewrite from version 11; a new index wants CREATE INDEX CONCURRENTLY, which cannot run inside a transaction block and leaves an invalid index behind if it fails.
- Express the answer as a range whose endpoints each trace to a stated assumption, then name the cheapest experiment that collapses it, which is almost always running one real batch against the real table and multiplying.
Follow-up
- How do you verify the backfill genuinely finished, given rows written by production traffic while it ran?
- Where does the backfill resume from after a worker is killed mid-batch, and what makes that resume point trustworthy?
Reverse your own decision and price the reversal
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
Approach
- State the original rationale without irony, in the version you would still defend given what was known then. If it is not defensible, the story is about carelessness rather than judgement, and a different example serves you better.
- Give the measurement that moved with a before and after: the p99 that did not improve, the cache hit rate that sat at 40%, the plan that flipped to a sequential scan once the table passed a size you can name.
- Cost the reversal in steps, not adjectives: expand-and-contract deploys, the dual-write window, the callers who had to be notified, the rows already written in the wrong shape that had to be backfilled or abandoned.
- Distinguish reversal from rewrite by naming what you kept. Most good reversals preserve the schema or the interface and undo one decision inside it, which is also why they were affordable.
Follow-up
- What in that decision was irreversible, and did you know it was irreversible when you made it?
- How did you tell the people who had already built on top of the original decision?
- 01
Explain your experience with technical writing and developer-oriented documentation.
- 02
You are asked to estimate a change you have never attempted: add a column to a 100-million-row table, populate it, move reads across, and drop the old shape. Give a range with the assumptions that generate it, including batch size, the signal your backfill throttles on, and wall-clock hours, and name the three unknowns that would move the number most. Then describe a real estimate you gave under comparable ignorance: how you expressed its uncertainty, what you committed to, and how wrong you turned out to be.
- 03
Describe a technical decision you made and later reversed. Pick one that cost something: a service you split and merged back, a cache you added and removed, an index you created that pushed the planner onto a worse plan, a projection you rebuilt from scratch. State what you believed when you decided, the measurement that changed your mind, how long the wrong version ran in production, and what the reversal cost in migrations, dual writes, and a deprecation window for callers you did not own.
How long does the typical interview process take?
The process is generally efficient. While it varies, many candidates move through the stages within a few weeks. The team values timely communication, so you should expect regular updates.
Imply Software Engineer candidate reports ↗What is the best way to prepare for the system design rounds?
Focus on the types of problems Imply solves: large-scale data ingestion, query performance, and distributed system reliability. Practice explaining your trade-offs and design decisions, as the "why" is just as important as the "how."
Imply Software Engineer candidate reports ↗Is the technical coding challenge representative of the day-to-day work?
Yes, Imply prioritizes practical, real-world problems. The challenges often mirror the types of tasks you would encounter when building features for Imply Lumi or the Apache Druid ecosystem.
Imply Software Engineer candidate reports ↗What differentiates a great candidate from a good one?
Successful candidates demonstrate not just technical proficiency, but also a "growth mindset." They are curious about how systems work under the hood and are able to collaborate effectively with their peers to solve ambiguous problems.
Imply Software Engineer candidate reports ↗What topics does Imply test in interviews?
Imply interviews most often cover SQL, Apache Druid / OLAP, Customer Success (At-risk customer management), PRD (Product Requirements Document), and Observability data. The exact emphasis depends on the specific role you apply for.
Imply Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Imply Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
Explore related engineering environments and compare the preparation focus.
Vercel Software Engineer Interview Guide
Debugging practice for Vercel software engineer prep built on a reproduction you trust, narrowing to the smallest failing input, and changing one variable per run so the result stays attributable.
WorldWide Technology Holding Software Engineer Interview Guide
What WorldWide Technology Holding asks Software Engineer candidates, from reported rounds and questions.
XYZ Software Engineer Interview Guide
What XYZ asks Software Engineer candidates, from reported rounds and questions.
Wellfit Technologies Software Engineer Interview Guide
What Wellfit Technologies asks Software Engineer candidates, from reported rounds and questions.