As a Software Engineer at NDA, you will design, build, and optimize high-impact systems that power our global operations. Our engineering ecosystem is diverse, ranging from highly resilient, cloud-native distributed microservices to specialized, ultra-low-latency platforms and hardware-integrated systems like Radio Frequency (RF) measurement software. Whether you are optimizing a high-frequency data pipeline or architecting domain-driven cloud architectures, your work directly influences NDA's core product capabilities and business scaling. Engineering at NDA is defined by technical rigor, collaboration, and a commitment to clean, maintainable architecture. You will work alongside industry experts to solve complex scalability challenges, manage high-throughput event streaming, and bridge the gap between sophisticated hardware measurements and software execution. It is a highly rewarding environment where technical depth is celebrated, and engineers are given the autonomy to drive end-to-end technical solutions.
Recruiter Sync
reportedInitial discussion with the recruiter to align on the role and expectations.
What to demonstrate
- Initial discussion with the recruiter to align on the role and expectations
- Depth in Event Sourcing
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 Discussions
reportedEngage in real-world technical discussions relevant to the engineering track.
What to demonstrate
- Engage in real-world technical discussions relevant to the engineering track
- Depth in Event Sourcing
How to prepare
- Answer aloud and timed: Describe how you would use Azure Service Bus and Redis to build a highly available, event-driven system.
- Answer aloud and timed: What are the trade-offs of using Event-Sourcing compared to traditional CRUD database designs?
Architectural Case Studies
reportedReview and analyze architectural case studies to assess problem-solving skills.
What to demonstrate
- Review and analyze architectural case studies to assess problem-solving skills
- Depth in Event Sourcing
How to prepare
- Answer aloud and timed: How do you handle distributed transactions and maintain eventual consistency across multiple microservices?
- Answer aloud and timed: How does the async-await pattern work under the hood in.NET Core, and how does it impact thread pool utilization?
Project Reviews
reportedDeep-dive into past projects to evaluate experience and technical capabilities.
What to demonstrate
- Deep-dive into past projects to evaluate experience and technical capabilities
- Depth in Event Sourcing
How to prepare
- Answer aloud and timed: What strategies do you use to minimize garbage collection latency in high-throughput applications?
- Answer aloud and timed: Describe a scenario where you had to optimize an algorithm to run in a low-latency environment. What profiling tools did you use?
Feedback Delivery
reportedReceive detailed and constructive feedback, typically within 24 hours.
What to demonstrate
- Receive detailed and constructive feedback, typically within 24 hours
- Depth in Event Sourcing
How to prepare
- Answer aloud and timed: How do you handle race conditions and thread synchronization without severely degrading system performance?
- Answer aloud and timed: Tell me about the most technically difficult project you have worked on. What made it challenging, and what was the outcome?
Final Offer Stage
reportedDiscussion regarding the final offer and next steps in the hiring process.
What to demonstrate
- Discussion regarding the final offer and next steps in the hiring process
- Depth in Event Sourcing
How to prepare
- Answer aloud and timed: How do you handle stress and tight deadlines when critical systems encounter production issues?
- Answer aloud and timed: Describe a time when you had to make a technical compromise to meet a business deadline. How did you manage technical debt?
PracHub editorial advice for the preparation topics above.
Master Domain-Driven Design
If you are interviewing for a distributed systems role, brush up on your DDD terminology. Be ready to explain the concrete differences between Entities and Value Objects, and how to define bounded contexts.
Prepare for Deep System Dives
Do not gloss over the details of your past projects. Your interviewers will ask guiding questions to see how deeply you understand the underlying runtime, memory management, and performance characteristics of the systems you built.
Designing for a scale nobody asked for
Ask for request rate, data size, read-to-write ratio and expected growth, then size the simplest option first; one relational instance on current hardware covers a large share of real workloads. Reaching for shards, queues and a cache tier before any number has been quoted reads as pattern-matching rather than judgement.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
How does the async-await pattern work under the hood in.NET Core, and how does it impact thread pool utilizati
How does the async-await pattern work under the hood in.NET Core, and how does it impact thread pool utilization?
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?
Describe a scenario where you had to optimize an algorithm to run in a low-latency environment. What profiling
Describe a scenario where you had to optimize an algorithm to run in a low-latency environment. What profiling tools did you use?
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 handle race conditions and thread synchronization without severely degrading system performance?
How do you handle race conditions and thread synchronization without severely degrading system performance?
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?
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?
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?
Explain the difference between an Entity and a Value Object in Domain-Driven Design (DDD).
Explain the difference between an Entity and a Value Object in Domain-Driven Design (DDD).
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 implement CQRS (Command Query Responsibility Segregation) in a microservices architecture?
How do you implement CQRS (Command Query Responsibility Segregation) in a microservices architecture?
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?
Describe how you would use Azure Service Bus and Redis to build a highly available, event-driven system.
Describe how you would use Azure Service Bus and Redis to build a highly available, event-driven 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?
What are the trade-offs of using Event-Sourcing compared to traditional CRUD database designs?
What are the trade-offs of using Event-Sourcing compared to traditional CRUD database designs?
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 handle distributed transactions and maintain eventual consistency across multiple microservices?
How do you handle distributed transactions and maintain eventual consistency across multiple microservices?
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 strategies do you use to minimize garbage collection latency in high-throughput applications?
What strategies do you use to minimize garbage collection latency in high-throughput applications?
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?
p99 jumped on one listing filter while p50 stayed flat
After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.
Approach
- Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
- Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
- Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
- Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.
Follow-up
- The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
- How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?
Built from the rounds and topics NDA candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the NDA loop
- Write out the reported sequence: Recruiter Sync, Technical Discussions, Architectural Case Studies, Project Reviews, Feedback Delivery, Final Offer Stage.
- 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 6 reported rounds, with the weakest marked.
02Work Event Sourcing
- Spend the session on Event Sourcing, which NDA candidates report being tested on.
- Write one worked example in Event Sourcing and time yourself on it.
Deliverable: One timed worked example in Event Sourcing.
03Work Domain-Driven Design (DDD)
- Spend the session on Domain-Driven Design (DDD), which NDA candidates report being tested on.
- Write one worked example in Domain-Driven Design (DDD) and time yourself on it.
Deliverable: One timed worked example in Domain-Driven Design (DDD).
04Work CQRS (Command Query Responsibility Segregation)
- Spend the session on CQRS (Command Query Responsibility Segregation), which NDA candidates report being tested on.
- Write one worked example in CQRS (Command Query Responsibility Segregation) and time yourself on it.
Deliverable: One timed worked example in CQRS (Command Query Responsibility Segregation).
05Answer out loud: Distributed Systems & Architecture
- Answer aloud, timed: Explain the difference between an Entity and a Value Object in Domain-Driven Design (DDD).
- Answer aloud, timed: How do you implement CQRS (Command Query Responsibility Segregation) in a microservices architecture?
Deliverable: Spoken answers to 2 reported Distributed Systems & Architecture question(s), under time.
06Answer out loud: Low-Latency & High-Performance Computing
- Answer aloud, timed: How does the async-await pattern work under the hood in.NET Core, and how does it impact thread pool utilization?
- Answer aloud, timed: What strategies do you use to minimize garbage collection latency in high-throughput applications?
Deliverable: Spoken answers to 2 reported Low-Latency & High-Performance Computing question(s), under time.
07Answer out loud: Behavioral & Project Execution
- Answer aloud, timed: Tell me about the most technically difficult project you have worked on. What made it challenging, and what was the outcome?
- Answer aloud, timed: How do you handle stress and tight deadlines when critical systems encounter production issues?
Deliverable: Spoken answers to 2 reported Behavioral & Project Execution 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.
Tell me about the most technically difficult project you have worked on. What made it challenging, and what wa
Tell me about the most technically difficult project you have worked on. What made it challenging, and what was the outcome?
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 stress and tight deadlines when critical systems encounter production issues?
How do you handle stress and tight deadlines when critical systems encounter production issues?
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 make a technical compromise to meet a business deadline. How did you manage te
Describe a time when you had to make a technical compromise to meet a business deadline. How did you manage 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?
How do you handle a situation where you disagree with a senior stakeholder or director on an architectural dec
How do you handle a situation where you disagree with a senior stakeholder or director on an architectural decision?
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
Tell me about the most technically difficult project you have worked on. What made it challenging, and what was the outcome?
- 02
How do you handle stress and tight deadlines when critical systems encounter production issues?
- 03
Describe a time when you had to make a technical compromise to meet a business deadline. How did you manage technical debt?
- 04
How do you handle a situation where you disagree with a senior stakeholder or director on an architectural decision?
How difficult is the Software Engineer interview process at NDA?
The difficulty ranges from average to highly challenging depending on the specific team. For cloud and distributed systems roles, the focus is on practical architecture and DDD. For low-latency or HFT-style teams, the technical rounds are exceptionally rigorous and require deep, fundamental computer science preparation.
NDA Software Engineer candidate reports ↗What is the typical timeline for receiving feedback?
NDA is known for outstanding candidate communication. Feedback is highly detailed and swift, frequently arriving the next business day. If a rejection occurs, the recruiting team often provides specific areas for growth and recommended study materials.
NDA Software Engineer candidate reports ↗Does NDA support relocation for software engineering roles?
Yes. For highly technical and specialized positions, NDA fully covers relocation packages to help successful candidates transition smoothly to our key engineering hubs.
NDA Software Engineer candidate reports ↗Is there a standardized coding test like LeetCode during the process?
No, NDA generally avoids standardized, automated coding platforms. Instead, we focus on interactive technical discussions, architectural case studies, and practical coding exercises that mirror the actual day-to-day challenges you will face on the job.
NDA Software Engineer candidate reports ↗What topics does NDA test in interviews?
NDA interviews most often cover QA Engineering, Event Sourcing, Domain-Driven Design (DDD), Test Automation, and CQRS (Command Query Responsibility Segregation). The exact emphasis depends on the specific role you apply for.
NDA 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.
- 01NDA 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
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