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Trunk Tools Software Engineer Interview Guide

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

The 60-second brief

At Trunk Tools, a Software Engineer plays a pivotal role in revolutionizing the construction industry through cutting-edge financial technology and artificial intelligence. The company focuses on addressing the construction labor shortage and productivity crisis by offering an AI-powered platform that manages incentives, processes complex documents, and streamlines payments for craft workers. As an engineer in this space, you will not just write code; you will build the foundational infrastructure that directly impacts the livelihoods of thousands of workers and the efficiency of multi-million-dollar construction projects. The engineering team at Trunk Tools is tasked with solving unique, highly complex problems.

This guide is scoped to a Software Engineer candidate at Trunk Tools.

Focus areas

  • Data Modeling
  • Code Strategy
  • Software Engineering Coding Interviews

The Trunk Tools interview process

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

Conversational Screen

Initial conversation to assess mutual alignment between the candidate and the company.

What to demonstrate

  • Initial conversation to assess mutual alignment between the candidate and the company
  • Depth in Data Modeling

How to prepare

  • Answer aloud and timed: How would you design a database schema to track construction projects, individual worker shifts, and dynamic incentive payouts?
  • Answer aloud and timed: What are the trade-offs between using a relational database versus a NoSQL database when handling unstructured project documentation?

Take-Home Assignment

Practical coding assignment to evaluate independent coding standards.

What to demonstrate

  • Practical coding assignment to evaluate independent coding standards
  • Depth in Data Modeling

How to prepare

  • Answer aloud and timed: How do you ensure data integrity and transaction safety when processing financial payouts across multiple third-party APIs?
  • Answer aloud and timed: Design a system that parses large PDF blueprints and extracts metadata asynchronously without blocking user-facing services.

Technical Evaluations

Deeper technical interviews focusing on hands-on coding and system design skills.

What to demonstrate

  • Deeper technical interviews focusing on hands-on coding and system design skills
  • Depth in Data Modeling

How to prepare

  • Answer aloud and timed: Given an open-ended requirement with no starter framework, how do you decide on your project structure and directory layout?
  • Answer aloud and timed: How do you write highly testable code when integrating with external payment gateways or AI models?

Leadership Discussions

Sessions with senior leadership to discuss strategy, product vision, and cultural fit.

What to demonstrate

  • Sessions with senior leadership to discuss strategy, product vision, and cultural fit
  • Depth in Data Modeling

How to prepare

  • Answer aloud and timed: Walk me through your process for optimizing a slow-running backend service that aggregates weekly earnings data.
  • Answer aloud and timed: How do you balance speed of delivery with code quality and technical debt in an early-stage engineering team?

Where candidates lose points

Over-communicate on your take-home

Since the take-home is completely open-ended, include a comprehensive README. Explain your architectural choices, database design trade-offs, how to run the project, and what you would improve if you had more time.

Be proactive with follow-ups

In a fast-moving startup environment, communication can occasionally experience delays. If you do not hear back within a few business days after completing a round or submitting an assignment, proactively send a polite follow-up email.

Showcase startup readiness

During behavioral rounds, highlight experiences where you had to wear multiple hats, self-manage, or build features from scratch with minimal guidance.

Focus on data integrity

When discussing system design, pay close attention to financial transactions, data consistency, and how your system handles partial failures or concurrent operations.

Questions & practice

Given an open-ended requirement with no starter framework, how do you decide on your project structure and dir

Medium · Coding Strategy & Execution

Given an open-ended requirement with no starter framework, how do you decide on your project structure and directory layout?

Approach

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

Follow-up

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

Walk me through your process for optimizing a slow-running backend service that aggregates weekly earnings dat

Medium · Coding Strategy & Execution

Walk me through your process for optimizing a slow-running backend service that aggregates weekly earnings data.

Approach

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

Follow-up

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

How do you balance speed of delivery with code quality and technical debt in an early-stage engineering team?

Medium · Coding Strategy & Execution

Approach

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

Follow-up

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

Stop tag and share joins from fanning out a page

Medium · join fan-out · semi-joins · many-to-many

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

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

  1. 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.
  2. 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.
  3. 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.
  4. 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?

How would you design a database schema to track construction projects, individual worker shifts, and dynamic i

Medium · Data Modeling & System Architecture

How would you design a database schema to track construction projects, individual worker shifts, and dynamic incentive payouts?

Approach

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

Follow-up

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

What are the trade-offs between using a relational database versus a NoSQL database when handling unstructured

Medium · Data Modeling & System Architecture

What are the trade-offs between using a relational database versus a NoSQL database when handling unstructured project documentation?

Approach

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

Follow-up

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

How do you ensure data integrity and transaction safety when processing financial payouts across multiple thir

Medium · Data Modeling & System Architecture

How do you ensure data integrity and transaction safety when processing financial payouts across multiple third-party APIs?

Approach

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

Follow-up

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

Design a system that parses large PDF blueprints and extracts metadata asynchronously without blocking user-fa

Medium · Data Modeling & System Architecture

Design a system that parses large PDF blueprints and extracts metadata asynchronously without blocking user-facing services.

Approach

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

Follow-up

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

How do you write highly testable code when integrating with external payment gateways or AI models?

Medium · Coding Strategy & Execution

Approach

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

Follow-up

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

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

  1. Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
  2. Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
  3. Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
  4. Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

  • The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
  • How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Trunk Tools candidates report.

Day 1 — Map the Trunk Tools loop

  • Write out the reported sequence: Conversational Screen, Take-Home Assignment, Technical Evaluations, Leadership Discussions.
  • For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

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

Day 2 — Work Data Modeling

  • Spend the session on Data Modeling, which Trunk Tools candidates report being tested on.
  • Write one worked example in Data Modeling and time yourself on it.

Deliverable: One timed worked example in Data Modeling.

Day 3 — Work Code Strategy

  • Spend the session on Code Strategy, which Trunk Tools candidates report being tested on.
  • Write one worked example in Code Strategy and time yourself on it.

Deliverable: One timed worked example in Code Strategy.

Day 4 — Work Software Engineering Coding Interviews

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

Deliverable: One timed worked example in Software Engineering Coding Interviews.

Day 5 — Answer out loud: Data Modeling & System Architecture

  • Answer aloud, timed: How would you design a database schema to track construction projects, individual worker shifts, and dynamic incentive payouts?
  • Answer aloud, timed: What are the trade-offs between using a relational database versus a NoSQL database when handling unstructured project documentation?

Deliverable: Spoken answers to 2 reported Data Modeling & System Architecture question(s), under time.

Day 6 — Answer out loud: Coding Strategy & Execution

  • Answer aloud, timed: Given an open-ended requirement with no starter framework, how do you decide on your project structure and directory layout?
  • Answer aloud, timed: How do you write highly testable code when integrating with external payment gateways or AI models?

Deliverable: Spoken answers to 2 reported Coding Strategy & Execution question(s), under time.

Day 7 — Answer out loud: Behavioral & Startup Alignment

  • Answer aloud, timed: Tell me about a time you had to deliver a high-priority feature with incomplete or rapidly changing requirements.
  • Answer aloud, timed: Describe a situation where you disagreed with a product manager's technical direction. How did you resolve it?

Deliverable: Spoken answers to 2 reported Behavioral & Startup Alignment question(s), under time.

Behavioural preparation

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

Tell me about a time you had to deliver a high-priority feature with incomplete or rapidly changing requiremen

Medium · Behavioral & Startup Alignment

Tell me about a time you had to deliver a high-priority feature with incomplete or rapidly changing requirements.

Approach

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

Follow-up

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

Describe a situation where you disagreed with a product manager's technical direction. How did you resolve it?

Medium · Behavioral & Startup Alignment

Approach

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

Follow-up

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

How do you manage your time and prioritize tasks when working autonomously on a highly open-ended project?

Medium · Behavioral & Startup Alignment

Approach

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

Follow-up

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

Share an experience where you made a significant technical mistake. What did you learn, and how did you remedi

Medium · Behavioral & Startup Alignment

Share an experience where you made a significant technical mistake. What did you learn, and how did you remediate it?

Approach

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

Follow-up

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