A Software Engineer at Nearmap is responsible for building and scaling the technology that powers city-scale aerial imagery and geospatial data. This is not a standard web development role; it involves managing massive datasets, optimizing cloud pipelines, and serving high-resolution visual content to thousands of clients worldwide. The systems you build must be highly performant, handling petabytes of raw aerial data to turn them into seamless 2D and 3D models. The engineering team works on sophisticated pipelines that process raw aerial data and make it instantly accessible via web applications and APIs. As a Software Engineer, your work directly impacts the speed, reliability, and precision of these pipelines, enabling industries like construction, insurance, and government to make critical decisions. You will work alongside a highly skilled group of engineers, geospatial experts, and product managers to solve unique technical challenges. Collaborating across multidisciplinary teams, you will build robust APIs and scalable cloud infrastructure. The problems you will solve are technically diverse and require a strong balance of clean coding, system architecture, and pragmatic problem-solving. This role is ideal for engineers who thrive on physical-world data challenges and want to build systems that operate at a massive scale.
Initial Conversation
reportedDiscuss the company's products, your previous experience, and the scope of the role with engineering leadership.
What to demonstrate
- Discuss the company's products, your previous experience, and the scope of the role with engineering leadership
- Depth in Python
How to prepare
- Answer aloud and timed: How would you design a system to ingest and process large-scale image files efficiently?
- Answer aloud and timed: What architectural patterns would you use to ensure high availability for an API serving geospatial data?
Practical Technical Test
reportedComplete a technical assessment that reflects daily work tasks.
What to demonstrate
- Complete a technical assessment that reflects daily work tasks
- Depth in Python
How to prepare
- Answer aloud and timed: How do you approach database selection when dealing with both structured metadata and massive unstructured spatial datasets?
- Answer aloud and timed: How do you optimize cloud-based storage costs when dealing with petabytes of historical imagery data?
Review Session
reportedDive deep into your implementation choices from the technical test.
What to demonstrate
- Dive deep into your implementation choices from the technical test
- Depth in Python
How to prepare
- Answer aloud and timed: Can you walk us through the design decisions of a project you recently built and show us some of the code?
- Answer aloud and timed: How do you optimize a resource-heavy Python script to handle concurrent processing?
Cross-Functional Round
reportedEvaluate your collaboration style in a round with cross-functional team members.
What to demonstrate
- Evaluate your collaboration style in a round with cross-functional team members
- Depth in Python
How to prepare
- Answer aloud and timed: What is your approach to writing testable, maintainable code, and how do you handle technical debt?
- Answer aloud and timed: Explain how you would implement a robust error-handling mechanism in a distributed data pipeline.
PracHub editorial advice for the preparation topics above.
Showcase your past work
Be ready to discuss and even display code from your previous projects. Nearmap values candidates who can confidently walk through their architectural decisions and code implementation.
Engage in a two-way dialogue
Avoid treating the interview as a one-sided interrogation. Ask questions about their product, their engineering challenges, and how they operate. This shows initiative and collaborative alignment.
Focus on the "why" behind your code
When discussing the technical test, don't just explain what the code does. Focus on why you chose a particular library, pattern, or optimization strategy.
Nearmap's technical test is highly practical
Expect to write code that mimics real-world scenarios rather than arbitrary algorithmic puzzles.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Can you walk us through the design decisions of a project you recently built and show us some of the code?
Can you walk us through the design decisions of a project you recently built and show us some of the code?
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?
How do you optimize a resource-heavy Python script to handle concurrent processing?
How do you optimize a resource-heavy Python script to handle concurrent processing?
Approach
- Say what the runtime actually does before reasoning about the code.
- Name what is shared across threads and what owns each piece of state.
- Identify the window where an invariant is briefly untrue.
- Distinguish a value from a reference to it, and say which one you handed out.
Follow-up
- What happens if two callers reach this at the same time?
- Where could this allocate more than you expect?
Track a rolling failure rate per destination for circuit decisions
The egress service delivers about 1,500 webhooks per second across roughly 40,000 destinations, each call bounded by a 10 second timeout. Maintain, per destination, the failure rate over the trailing 60 seconds so a caller can ask before dispatch whether the circuit should open. Attempts arrive as (destination_id, finished_at_ms, outcome). Requirement: amortised O(1) per attempt, with total memory bounded by the destination count rather than by traffic. Give the structure, its exact memory, and the rule that stops a destination with three attempts from opening a circuit.
Approach
- Name the exact-deque version and then reject it as the default. Holding timestamps and advancing a tail pointer past anything older than now minus 60 seconds is a correct two-pointer window at amortised O(1) per attempt, but its memory tracks in-window traffic, so one destination in a retry storm holds hundreds of thousands of entries while thousands of quiet destinations hold none.
- Use a ring of 60 one-second buckets per destination, each bucket a pair of counters for attempts and failures. On an attempt, advance the ring by the elapsed whole seconds, zeroing at most min(elapsed, 60) buckets, then increment the head. That is amortised O(1) with a fixed footprint per destination.
- State the footprint: 60 buckets times two 4-byte counters is 480 bytes of payload per destination, so 40,000 destinations is roughly 20 to 25 MB with per-entry overhead, bounded by the catalogue rather than by the rate. The cost is granularity, since the oldest bucket ages out in whole seconds, which is far tighter than the decision needs.
- Require a minimum sample before the circuit may open. A destination with three attempts and three failures reads as 100 percent and is not evidence; a floor of roughly 20 attempts in the window makes the ratio meaningful, and below that floor use a run of consecutive failures as the trigger instead.
Follow-up
- The fleet is 30 instances and each sees roughly a thirtieth of a destination's traffic. Where does the rate actually live, and what does a per-instance answer get wrong?
- A destination answers in 9.5 seconds and succeeds. It is not failing but it is consuming your per-destination concurrency. What signal should open the circuit here?
Stop tag and share joins from fanning out a page
resource_tag is (resource_id, tag_id) with PK (resource_id, tag_id); resource_share is (resource_id, shared_with_user_id, permission). The tagged-and-shared listing inner-joins resource to both, filters tenant_id, tag_id = ANY($2) and shared_with_user_id = $3, orders by updated_at DESC and takes 50. Pages come back with fewer than 50 distinct resources and the total in the header is far too high. Explain the row multiplication, rewrite both the page query and the count query so each is correct, and name the index each one needs. PostgreSQL 16.
Approach
- Do the arithmetic against the predicates that are actually there. An inner join emits one row per matching child row, and both joins are filtered: tag_id = ANY($2) admits only the requested tags, shared_with_user_id = $3 admits one user's share rows. So a resource holding three of the requested tags and shared with $3 once yields three rows, not one — the multiplier is its count of matching tags times its share rows for that single user, and that second factor is 1 unless the table admits duplicate (resource_id, shared_with_user_id) pairs. LIMIT 50 then limits rows rather than resources, and COUNT(*) counts pairs — the header is the product, not the population.
- Reject DISTINCT as the fix. It deduplicates after the product has been built, so the planner must materialise and sort the fanned-out set before the LIMIT can apply, and it leaves any SUM or AVG in the same select list wrong.
- Rewrite both filters as semi-joins, keeping resource as the only row source: AND EXISTS (SELECT 1 FROM resource_tag rt WHERE rt.resource_id = r.resource_id AND rt.tag_id = ANY($2)) and the same shape against resource_share. A semi-join stops at the first match per resource and preserves the driving index order, so ORDER BY updated_at DESC, resource_id DESC LIMIT 50 still stops after 50 rows.
- Count with the same predicates and no join at all: SELECT count(*) FROM resource r WHERE r.tenant_id = $1 AND r.status = 'active' AND EXISTS (...) AND EXISTS (...). Nothing multiplies a resource, so the number is the population.
Follow-up
- The filter changes from 'any of these tags' to 'all of these tags'. Rewrite it and state what it costs relative to the ANY form.
- A resource can be shared with the same user twice under different permissions. Does your count change, and should it?
Explain why the owner filter ignores the listing index
The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.
Approach
- Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
- For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
- Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
- Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.
Follow-up
- 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
- A dashboard runs this for 40 owners in one page load. What changes about the design?
How would you design a system to ingest and process large-scale image files efficiently?
How would you design a system to ingest and process large-scale image files efficiently?
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?
What architectural patterns would you use to ensure high availability for an API serving geospatial data?
What architectural patterns would you use to ensure high availability for an API serving geospatial data?
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 do you approach database selection when dealing with both structured metadata and massive unstructured spa
How do you approach database selection when dealing with both structured metadata and massive unstructured spatial datasets?
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 do you optimize cloud-based storage costs when dealing with petabytes of historical imagery data?
How do you optimize cloud-based storage costs when dealing with petabytes of historical imagery data?
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 how you would implement a robust error-handling mechanism in a distributed data pipeline.
Explain how you would implement a robust error-handling mechanism in a distributed data pipeline.
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?
One log partition stops advancing while the others drain
Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.
Approach
- Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
- Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
- Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
- Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.
Follow-up
- The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
- How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?
Built from the rounds and topics Nearmap candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Nearmap loop
- Write out the reported sequence: Initial Conversation, Practical Technical Test, Review Session, Cross-Functional Round.
- 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 Python
- Spend the session on Python, which Nearmap candidates report being tested on.
- Write one worked example in Python and time yourself on it.
Deliverable: One timed worked example in Python.
03Work Architectural Approach
- Spend the session on Architectural Approach, which Nearmap candidates report being tested on.
- Write one worked example in Architectural Approach and time yourself on it.
Deliverable: One timed worked example in Architectural Approach.
04Work Coding Practice / Writing Code
- Spend the session on Coding Practice / Writing Code, which Nearmap candidates report being tested on.
- Write one worked example in Coding Practice / Writing Code and time yourself on it.
Deliverable: One timed worked example in Coding Practice / Writing Code.
05Answer out loud: Architectural & System Design
- Answer aloud, timed: How would you design a system to ingest and process large-scale image files efficiently?
- Answer aloud, timed: What architectural patterns would you use to ensure high availability for an API serving geospatial data?
Deliverable: Spoken answers to 2 reported Architectural & System Design question(s), under time.
06Answer out loud: Practical Coding & Implementation
- Answer aloud, timed: Can you walk us through the design decisions of a project you recently built and show us some of the code?
- Answer aloud, timed: How do you optimize a resource-heavy Python script to handle concurrent processing?
Deliverable: Spoken answers to 2 reported Practical Coding & Implementation question(s), under time.
07Answer out loud: Behavioral & Collaborative
- Answer aloud, timed: Describe a time when you had to collaborate with a product manager or designer who had a different perspective on a feature.
- Answer aloud, timed: How do you handle constructive, direct feedback on your code or architectural proposals?
Deliverable: Spoken answers to 2 reported Behavioral & Collaborative 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.
What is your approach to writing testable, maintainable code, and how do you handle technical debt?
What is your approach to writing testable, maintainable code, and how do you handle technical debt?
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?
Describe a time when you had to collaborate with a product manager or designer who had a different perspective
Describe a time when you had to collaborate with a product manager or designer who had a different perspective on a feature.
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?
How do you handle constructive, direct feedback on your code or architectural proposals?
How do you handle constructive, direct feedback on your code or architectural proposals?
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?
Can you share an experience where you had to quickly adapt to a major change in project requirements?
Can you share an experience where you had to quickly adapt to a major change in project requirements?
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?
Tell me about a time you had to explain a complex technical issue to a non-technical stakeholder.
Tell me about a time you had to explain a complex technical issue to a non-technical stakeholder.
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?
- 01
What is your approach to writing testable, maintainable code, and how do you handle technical debt?
- 02
Describe a time when you had to collaborate with a product manager or designer who had a different perspective on a feature.
- 03
How do you handle constructive, direct feedback on your code or architectural proposals?
- 04
Can you share an experience where you had to quickly adapt to a major change in project requirements?
How difficult is the Nearmap interview process?
The interview process is generally considered average in difficulty. It focuses heavily on practical engineering skills, system architecture, and real-world scenarios rather than highly abstract, competitive-programming style puzzles.
Nearmap Software Engineer candidate reports ↗What is the typical timeline from the first screen to an offer?
Nearmap is known for a highly efficient interview loop. The entire process can often be completed in a couple of weeks, and in some cases, the interview rounds themselves take only a few days to conclude.
Nearmap Software Engineer candidate reports ↗What is the working style and culture like for engineers?
The culture is collaborative, direct, and highly focused on product impact. Teams operate with a high degree of autonomy, and engineers are encouraged to share honest feedback and take ownership of their projects.
Nearmap Software Engineer candidate reports ↗Does Nearmap offer remote or hybrid working arrangements?
Yes, Nearmap typically supports flexible hybrid working arrangements, allowing engineers to balance focused remote work with collaborative in-office sessions, depending on the specific team and location.
Nearmap Software Engineer candidate reports ↗What topics does Nearmap test in interviews?
Nearmap interviews most often cover Stakeholder Management, Problem Solving, Software Engineering Fundamentals, Communication Skills, and Cross-Functional Collaboration. The exact emphasis depends on the specific role you apply for.
Nearmap 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.
- 01Nearmap 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.
Lambda Software Engineer Interview Guide
Failure-handling practice for Lambda software engineer candidates: requests whose outcome a timeout leaves unknown, where a retry is safe, and deduplicating at the receiver instead of assuming exactly-once delivery.
CoreWeave Software Engineer Interview Guide
Failure-handling practice for CoreWeave software engineer candidates: requests whose outcome a timeout leaves unknown, where a retry is safe, and deduplicating at the receiver instead of assuming exactly-once delivery.
Harvey Software Engineer Interview Questions & Guide 2026
Prepare for the software engineer interview at Harvey with original coding, system design and debugging exercises, each paired with a worked solution and the reasoning behind it.
Western Alliance Bank Software Engineer Interview Guide
What Western Alliance Bank asks Software Engineer candidates, from reported rounds and questions.