As a Software Engineer at dv01, you are at the heart of transforming how the structured finance market interacts with data. dv01 provides unparalleled transparency and intelligence into consumer lending markets, and your role is critical in building the scalable platforms and intuitive interfaces that make this possible. You will be responsible for designing, developing, and deploying end-to-end software solutions that empower financial institutions to make data-driven decisions. This position directly impacts the core product experience. Whether you are engineering robust backend services to process massive datasets, parsing complex CSV files, or building dynamic, responsive user interfaces to visualize loan performance, your work will be highly visible. You will collaborate closely with product managers, engineering leaders, and data experts to translate complex financial workflows into seamless digital experiences. Expect a role that balances technical complexity with product ownership. dv01 values engineers who care deeply about craftsmanship, user experience, and practical problem-solving. You will not just be writing code; you will be making critical architectural decisions, navigating edge cases in real-world data, and ensuring the platform remains performant and reliable at scale.
Recruiter Phone Screen
reportedInitial conversation with a recruiter to discuss your background and the role.
What to demonstrate
- Initial conversation with a recruiter to discuss your background and the role
- Depth in React.js
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 Conversation
reportedDiscussion with an engineering leader or VP focusing on technical skills and experience.
What to demonstrate
- Discussion with an engineering leader or VP focusing on technical skills and experience
- Depth in React.js
How to prepare
- Answer aloud and timed: If we needed to scale this application to handle a dataset 100x larger, what specific changes would you make to both the frontend and backend?
- Answer aloud and timed: Let's look at this specific React component. How would you refactor it to improve rendering performance?
Take-Home Assignment
reportedComplete a substantial take-home project, often involving full-stack or frontend-heavy tasks.
What to demonstrate
- Complete a substantial take-home project, often involving full-stack or frontend-heavy tasks
- Depth in React.js
How to prepare
- Answer aloud and timed: How would you implement robust error handling and logging for a Node service that parses large CSV files daily?
- Answer aloud and timed: Tell me about a time you discovered a critical bug in a feature right before launch. How did you handle it?
Onsite Loop
reportedParticipate in an onsite interview that includes a product demo, technical review, and behavioral rounds.
What to demonstrate
- Participate in an onsite interview that includes a product demo, technical review, and behavioral rounds
- Depth in React.js
How to prepare
- Answer aloud and timed: Describe a situation where you strongly disagreed with a Product Manager about a technical implementation. How did you resolve the conflict?
- Answer aloud and timed: Tell me about a time you had to work with a poorly documented or broken internal API. What steps did you take to move the project forward?
PracHub editorial advice for the preparation topics above.
Over-communicate in your README
The team evaluates your documentation as strictly as your code. If you make a specific product decision—such as calculating totals based only on the current paginated view rather than the entire dataset—explain exactly why you made that choice in your README and in code comments.
Defend your choices respectfully
During the onsite review, interviewers will challenge your implementation. They want to see if you can engage in a healthy technical debate. Defend your trade-offs, but show a willingness to adapt if they present a valid counter-argument.
Prepare for the product demo
The onsite loop often includes a demo of the dv01 platform. Pay close attention. Understanding how their clients use data tables and filtering in real life will give you a massive advantage when discussing how to build those same features during your technical rounds.
Test your code extensively
Do not submit your take-home assignment without writing tests. Even if you only write tests for a thin slice of the application, demonstrating that you care about test coverage and craftsmanship will set you apart from candidates who only focus on feature delivery.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Archive a resource graph without breaking live references or recursing
Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.
Approach
- Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
- Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
- Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
- Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.
Follow-up
- The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
- The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?
Identify the heaviest tenants in a five-minute window under memory pressure
The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.
Approach
- Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
- Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
- State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
- Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.
Follow-up
- The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
- Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?
Merge partitioned event streams into one ordered feed with bounded lateness
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
- 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.
- 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.
- 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.
- 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?
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?
Denormalise tenant onto revisions and backfill it live
resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.
Approach
- Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
- Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
- Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
- Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.
Follow-up
- The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
- How do you verify the backfill actually finished, given rows are still being inserted while it runs?
Walk me through the architecture of the take-home application you submitted. Why did you choose this specific
Walk me through the architecture of the take-home application you submitted. Why did you choose this specific state management approach?
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 did you handle the edge cases in the mock API provided? What would happen to your UI if the endpoint retur
How did you handle the edge cases in the mock API provided? What would happen to your UI if the endpoint returned a null value for a required field?
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- 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?
If we needed to scale this application to handle a dataset 100x larger, what specific changes would you make t
If we needed to scale this application to handle a dataset 100x larger, what specific changes would you make to both the frontend and backend?
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?
Let's look at this specific React component. How would you refactor it to improve rendering performance?
Let's look at this specific React component. How would you refactor it to improve rendering 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?
How would you implement robust error handling and logging for a Node service that parses large CSV files daily
How would you implement robust error handling and logging for a Node service that parses large CSV files daily?
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?
Every query on one table stalls for forty seconds mid-deploy
During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.
Approach
- Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
- Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
- Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
- Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.
Follow-up
- The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
- Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?
Built from the rounds and topics dv01 candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the dv01 loop
- Write out the reported sequence: Recruiter Phone Screen, Technical Conversation, Take-Home Assignment, Onsite Loop.
- 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 React.js
- Spend the session on React.js, which dv01 candidates report being tested on.
- Write one worked example in React.js and time yourself on it.
Deliverable: One timed worked example in React.js.
03Work Data filtering & table UIs
- Spend the session on Data filtering & table UIs, which dv01 candidates report being tested on.
- Write one worked example in Data filtering & table UIs and time yourself on it.
Deliverable: One timed worked example in Data filtering & table UIs.
04Work JavaScript (Node.js)
- Spend the session on JavaScript (Node.js), which dv01 candidates report being tested on.
- Write one worked example in JavaScript (Node.js) and time yourself on it.
Deliverable: One timed worked example in JavaScript (Node.js).
05Answer out loud: Practical Coding and Architecture
- Answer aloud, timed: Walk me through the architecture of the take-home application you submitted. Why did you choose this specific state management approach?
- Answer aloud, timed: How did you handle the edge cases in the mock API provided? What would happen to your UI if the endpoint returned a null value for a required field?
Deliverable: Spoken answers to 2 reported Practical Coding and Architecture question(s), under time.
06Answer out loud: Behavioral and Cross-Functional
- Answer aloud, timed: Tell me about a time you discovered a critical bug in a feature right before launch. How did you handle it?
- Answer aloud, timed: Describe a situation where you strongly disagreed with a Product Manager about a technical implementation. How did you resolve the conflict?
Deliverable: Spoken answers to 2 reported Behavioral and Cross-Functional question(s), under time.
07Dry run for dv01
- Run one full mock under time, then write down the two questions you most want to ask your interviewers.
Deliverable: A completed timed mock and two questions to ask.
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 a time you discovered a critical bug in a feature right before launch. How did you handle it?
Tell me about a time you discovered a critical bug in a feature right before launch. How did you handle it?
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 situation where you strongly disagreed with a Product Manager about a technical implementation. How
Describe a situation where you strongly disagreed with a Product Manager about a technical implementation. How did you resolve the conflict?
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 work with a poorly documented or broken internal API. What steps did you take
Tell me about a time you had to work with a poorly documented or broken internal API. What steps did you take to move the project forward?
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?
What would you do if you realized halfway through a sprint that a feature was vastly more complex than origina
What would you do if you realized halfway through a sprint that a feature was vastly more complex than originally estimated?
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 piece of code or a project you are particularly proud of. What made it a success?
Tell me about a piece of code or a project you are particularly proud of. What made it a success?
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 a time you discovered a critical bug in a feature right before launch. How did you handle it?
- 02
Describe a situation where you strongly disagreed with a Product Manager about a technical implementation. How did you resolve the conflict?
- 03
Tell me about a time you had to work with a poorly documented or broken internal API. What steps did you take to move the project forward?
- 04
What would you do if you realized halfway through a sprint that a feature was vastly more complex than originally estimated?
Does dv01 ask difficult Leetcode-style algorithms during the interview?
No. dv01 focuses on practical, end-to-end software engineering. You are much more likely to be evaluated on your ability to build a React interface, parse a CSV file in Node, and design a functional API than on your ability to invert a binary tree or solve dynamic programming puzzles.
dv01 Software Engineer candidate reports ↗How much time should I expect to spend on the take-home assignment?
Candidates typically report spending 4 to 6 hours on the take-home assignment. It is crucial to balance completeness with time management. Focus on delivering a working, bug-free core experience, and use your README to explain how you would build out the remaining features if you had more time.
dv01 Software Engineer candidate reports ↗What happens if the API provided for the take-home assignment is broken or returns bad data?
Treat it as a feature, not a bug. Real-world APIs often return incomplete or malformed data. Write defensive code to handle skipped rows or empty instances gracefully, and clearly document these edge cases and your handling strategy in your README.
dv01 Software Engineer candidate reports ↗How quickly does dv01 provide feedback after interview rounds?
dv01 is known for moving quickly when they find strong candidates. Some candidates have reported receiving feedback within minutes of preliminary rounds and final decisions within 24 hours of completing the onsite loop.
dv01 Software Engineer candidate reports ↗Is this role remote or hybrid?
The Software Engineer role is frequently offered as a remote position, though dv01 also has a strong presence in New York. You should clarify your location preferences and working hours with your recruiter during the initial phone screen.
dv01 Software Engineer candidate reports ↗What topics does dv01 test in interviews?
dv01 interviews most often cover SQL, Python, BigQuery, Apache Spark, and Problem Solving. The exact emphasis depends on the specific role you apply for.
dv01 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.
- 01dv01 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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