cswork / libraryOpen practice workspace

Interview guides · Holman Automotive Group

Holman Automotive Group Software Engineer Interview Guide

Holman Automotive Group Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

At Holman Automotive Group, a Software Engineer does not just write code; they build the digital engine that drives one of the largest integrated automotive service organizations in North America. From retail dealerships and vehicle fleet leasing to manufacturing and logistics, the software ecosystem here is vast, complex, and highly integrated. Engineers are responsible for designing, deploying, and maintaining scalable systems that optimize global supply chains, automate fleet management workflows, and modernize customer-facing retail platforms. The impact of your work in this role is immediate and measurable.

This guide is scoped to a Software Engineer candidate at Holman Automotive Group.

Focus areas

  • Microsoft Dynamics 365 (D365)
  • Data Structures & Algorithms (DSA)
  • Cloud Engineering (General)

The Holman Automotive Group interview process

Holman Automotive Group candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Phone Screen

Focus on your background, career goals, and alignment with the team's current needs.

What to demonstrate

  • Focus on your background, career goals, and alignment with the team's current needs
  • Depth in Microsoft Dynamics 365 (D365)

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 Evaluation

Deep dive into your resume, discussion of past projects, and a short live coding or DSA assessment.

What to demonstrate

  • Deep dive into your resume, discussion of past projects, and a short live coding or DSA assessment
  • Depth in Microsoft Dynamics 365 (D365)

How to prepare

  • Answer aloud and timed: Describe a complex technical challenge you faced on a project listed on your resume and how you resolved it.
  • Answer aloud and timed: What are the key differences between SQL and NoSQL databases, and how do you decide which to use for a new service?

Multi-Dimensional Discussion

Covers team culture and advanced technical concepts, with potential technical deep dives.

What to demonstrate

  • Covers team culture and advanced technical concepts
  • With potential technical deep dives

How to prepare

  • Answer aloud and timed: How do you approach writing clean, maintainable code when working with legacy enterprise software?
  • Answer aloud and timed: Given an array of integers, write an algorithm to find the subarray with the largest sum.

Where candidates lose points

Master your resume

Expect interviewers to ask highly detailed questions about the technologies, architectures, and outcomes listed on your resume. If it is on your resume, it is fair game.

Over-communicate during coding exercises

Do not code in silence. Talk through your approach, explain why you are choosing a specific data structure, and discuss potential optimizations as you write your solution.

Prepare for unexpected technical depth

Even if a round is labeled as a "culture fit" or "HR discussion," remain technically prepared. Keep your system design and architectural knowledge sharp until the very end of the process.

Highlight your business impact

Holman Automotive Group values engineers who understand how their code drives business outcomes. Frame your past achievements in terms of efficiency gained, costs reduced, or system reliability improved.

Questions & practice

Given an array of integers, write an algorithm to find the subarray with the largest sum.

Medium · Coding & Problem Solving

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?

Implement a search algorithm to locate a specific vehicle record within a large, unsorted dataset.

Medium · Coding & Problem Solving

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?

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

  1. Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
  2. Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
  3. For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
  4. Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

  • The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
  • Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

What are the key differences between SQL and NoSQL databases, and how do you decide which to use for a new ser

Medium · Technical & Domain-Specific Questions

What are the key differences between SQL and NoSQL databases, and how do you decide which to use for a new service?

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. Handle the rows that do not match: that is usually the actual question.

Follow-up

  • How does the query change if that join becomes one-to-many?
  • What happens to this when the table is ten times larger?

Explain how you would optimize a slow-running database query that retrieves fleet logistics data.

Medium · Coding & Problem Solving

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. Handle the rows that do not match: that is usually the actual question.

Follow-up

  • How does the query change if that join becomes one-to-many?
  • What happens to this when the table is ten times larger?

Explain the lifecycle of a request in a modern web application, from the browser to the database.

Medium · Technical & Domain-Specific Questions

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

Describe a complex technical challenge you faced on a project listed on your resume and how you resolved it.

Medium · Technical & Domain-Specific Questions

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

How do you approach writing clean, maintainable code when working with legacy enterprise software?

Medium · Technical & Domain-Specific Questions

Approach

  1. Clarify what is being asked and what a complete answer contains.
  2. State your assumptions explicitly before working the problem.
  3. Say what you would check first and why it is the highest-information step.
  4. Work from the requirement backwards to the design.

Follow-up

  • What assumption would you test first?
  • How would you know your answer was wrong?

How would you design a function to validate if a given string has balanced parentheses?

Medium · Coding & Problem Solving

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?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

  1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
  2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
  3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
  4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

  • Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
  • How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Holman Automotive Group candidates report.

Day 1 — Map the Holman Automotive Group loop

  • Write out the reported sequence: Recruiter Phone Screen, Technical Evaluation, Multi-Dimensional Discussion.
  • For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work Microsoft Dynamics 365 (D365)

  • Spend the session on Microsoft Dynamics 365 (D365), which Holman Automotive Group candidates report being tested on.
  • Write one worked example in Microsoft Dynamics 365 (D365) and time yourself on it.

Deliverable: One timed worked example in Microsoft Dynamics 365 (D365).

Day 3 — Work Data Structures & Algorithms (DSA)

  • Spend the session on Data Structures & Algorithms (DSA), which Holman Automotive Group candidates report being tested on.
  • Write one worked example in Data Structures & Algorithms (DSA) and time yourself on it.

Deliverable: One timed worked example in Data Structures & Algorithms (DSA).

Day 4 — Work Cloud Engineering (General)

  • Spend the session on Cloud Engineering (General), which Holman Automotive Group candidates report being tested on.
  • Write one worked example in Cloud Engineering (General) and time yourself on it.

Deliverable: One timed worked example in Cloud Engineering (General).

Day 5 — Answer out loud: Technical & Domain-Specific Questions

  • Answer aloud, timed: Explain the lifecycle of a request in a modern web application, from the browser to the database.
  • Answer aloud, timed: How do you handle state management and data consistency across distributed systems?

Deliverable: Spoken answers to 2 reported Technical & Domain-Specific Questions question(s), under time.

Day 6 — Answer out loud: Coding & Problem Solving

  • Answer aloud, timed: Given an array of integers, write an algorithm to find the subarray with the largest sum.
  • Answer aloud, timed: How would you design a function to validate if a given string has balanced parentheses?

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

Day 7 — Answer out loud: Behavioral & Alignment Questions

  • Answer aloud, timed: Describe a time when you had a disagreement with a product manager or tech lead. How did you resolve it?
  • Answer aloud, timed: How do you prioritize tasks when working on multiple high-priority deliverables simultaneously?

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

Behavioural preparation

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

How do you handle state management and data consistency across distributed systems?

Medium · Technical & Domain-Specific Questions

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 time when you had a disagreement with a product manager or tech lead. How did you resolve it?

Medium · Behavioral & Alignment Questions

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 prioritize tasks when working on multiple high-priority deliverables simultaneously?

Medium · Behavioral & Alignment Questions

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?

Tell me about a time you had to learn a new technology quickly to meet a project deadline.

Medium · Behavioral & Alignment Questions

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 ensure your code is thoroughly tested before it is deployed to a production environment?

Medium · Behavioral & Alignment Questions

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?