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Lozier Software Engineer Interview Guide

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

The 60-second brief

At Lozier, a Software Engineer occupies a unique and highly impactful position at the intersection of enterprise software development and advanced manufacturing. As North America's leading manufacturer of store fixtures and retail shelving, Lozier relies on robust, scalable, and highly integrated software systems to power its massive production facilities, automate complex design pipelines, and manage intricate supply chain logistics. The code you write doesn't just run in the cloud; it directly orchestrates physical operations across plants in Omaha, Joplin, and Middlebury. You will design, develop, and maintain systems that integrate directly with manufacturing execution systems (MES), enterprise resource planning (ERP) platforms, and custom CAD/CAM automation tools.

This guide is scoped to a Software Engineer candidate at Lozier.

Focus areas

  • Manufacturing Domain Knowledge
  • Industrial Engineering Domain Knowledge
  • Design Engineering (Design Engineering II)

The Lozier interview process

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

Phone Screen

Initial phone screen with HR to discuss background, interest in the company, and basic qualifications.

What to demonstrate

  • Initial phone screen with HR to discuss background, interest in the company, and basic qualifications
  • Depth in Manufacturing Domain Knowledge

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 Screen

Technical screen conducted by a hiring manager, including a self-rating exercise of technical skills and discussion of engineering experience.

What to demonstrate

  • Technical screen conducted by a hiring manager
  • Including a self-rating exercise of technical skills and discussion of engineering experience

How to prepare

  • Answer aloud and timed: How do you approach optimizing a slow-running SQL query or database transaction in a production environment?
  • Answer aloud and timed: Explain how you would write software to parse and automate design files (like CAD data) for manufacturing equipment.

In-Person Interview Loop

Comprehensive in-person interviews with key stakeholders, including a facility tour to understand the manufacturing environment.

What to demonstrate

  • Comprehensive in-person interviews with key stakeholders
  • Including a facility tour to understand the manufacturing environment

How to prepare

  • Answer aloud and timed: On a scale of 1 to 10, how would you rate your expertise in our primary development stack, and what would it take for you to reach a 10?
  • Answer aloud and timed: Walk me through a complex technical challenge you faced in a previous role and how you determined the best solution.

Where candidates lose points

Embrace the History

During your plant tour, pay close attention to the company's history and manufacturing milestones. Referencing things you saw or learned during the tour in your subsequent interview rounds shows exceptional engagement and cultural fit.

Be Honest with Your Self-Ratings

When asked to rate your skills, be completely honest. If you rate yourself as a 9 out of 10 in SQL, expect deep, highly specific questions about query optimization and database locks. It is far better to rate yourself a realistic 7 and explain your areas for growth than to overstate your expertise.

Asking thoughtful questions during your interviews is highly valued at Lozier

Prepare 3 to 4 questions for each interviewer, tailored to their specific roles—such as asking the Plant Manager about their biggest operational bottlenecks, or asking the Engineering Manager about their technical debt strategy.

Questions & practice

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

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

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

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

  1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
  2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
  3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
  4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

  • A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
  • Where does the fingerprint get computed relative to request decompression and the body-size limit?

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?

How do you approach optimizing a slow-running SQL query or database transaction in a production environment?

Medium · Technical & Systems Integration

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?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

  1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
  2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
  3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
  4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

  • A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
  • The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

How would you design a database schema to track inventory across multiple manufacturing plants in real-time?

Medium · Technical & Systems Integration

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?

Explain how you would write software to parse and automate design files (like CAD data) for manufacturing equi

Medium · Technical & Systems Integration

Explain how you would write software to parse and automate design files (like CAD data) for manufacturing equipment.

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?

On a scale of 1 to 10, how would you rate your expertise in our primary development stack, and what would it t

Medium · Self-Assessment & Domain Expertise

On a scale of 1 to 10, how would you rate your expertise in our primary development stack, and what would it take for you to reach a 10?

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?

Walk me through a complex technical challenge you faced in a previous role and how you determined the best sol

Medium · Self-Assessment & Domain Expertise

Walk me through a complex technical challenge you faced in a previous role and how you determined the best solution.

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?

What technologies or frameworks are you most comfortable with, and how do you decide when to adopt a new techn

Medium · Self-Assessment & Domain Expertise

What technologies or frameworks are you most comfortable with, and how do you decide when to adopt a new technology versus sticking with a proven one?

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

  1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
  2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
  3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
  4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

  • The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
  • How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics Lozier candidates report.

Day 1 — Map the Lozier loop

  • Write out the reported sequence: Phone Screen, Technical Screen, In-Person Interview 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 3 reported rounds, with the weakest marked.

Day 2 — Work Manufacturing Domain Knowledge

  • Spend the session on Manufacturing Domain Knowledge, which Lozier candidates report being tested on.
  • Write one worked example in Manufacturing Domain Knowledge and time yourself on it.

Deliverable: One timed worked example in Manufacturing Domain Knowledge.

Day 3 — Work Industrial Engineering Domain Knowledge

  • Spend the session on Industrial Engineering Domain Knowledge, which Lozier candidates report being tested on.
  • Write one worked example in Industrial Engineering Domain Knowledge and time yourself on it.

Deliverable: One timed worked example in Industrial Engineering Domain Knowledge.

Day 4 — Work Design Engineering (Design Engineering II)

  • Spend the session on Design Engineering (Design Engineering II), which Lozier candidates report being tested on.
  • Write one worked example in Design Engineering (Design Engineering II) and time yourself on it.

Deliverable: One timed worked example in Design Engineering (Design Engineering II).

Day 5 — Answer out loud: Technical & Systems Integration

  • Answer aloud, timed: How would you design a database schema to track inventory across multiple manufacturing plants in real-time?
  • Answer aloud, timed: Describe your experience with enterprise-level systems integration, such as connecting custom software to an ERP or MES.

Deliverable: Spoken answers to 2 reported Technical & Systems Integration question(s), under time.

Day 6 — Answer out loud: Self-Assessment & Domain Expertise

  • Answer aloud, timed: On a scale of 1 to 10, how would you rate your expertise in our primary development stack, and what would it take for you to reach a 10?
  • Answer aloud, timed: Walk me through a complex technical challenge you faced in a previous role and how you determined the best solution.

Deliverable: Spoken answers to 2 reported Self-Assessment & Domain Expertise question(s), under time.

Day 7 — Answer out loud: Behavioral & Cultural Fit

  • Answer aloud, timed: Tell me about a time you had to work with non-technical stakeholders, such as plant managers or assembly line workers, to gather requirements for a software tool.
  • Answer aloud, timed: How do you handle a situation where a critical production system goes down, and you have conflicting opinions on how to resolve the issue?

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

Behavioural preparation

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

Describe your experience with enterprise-level systems integration, such as connecting custom software to an E

Medium · Technical & Systems Integration

Describe your experience with enterprise-level systems integration, such as connecting custom software to an ERP or MES.

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 work with non-technical stakeholders, such as plant managers or assembly line

Medium · Behavioral & Cultural Fit

Tell me about a time you had to work with non-technical stakeholders, such as plant managers or assembly line workers, to gather requirements for a software tool.

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 handle a situation where a critical production system goes down, and you have conflicting opinions

Medium · Behavioral & Cultural Fit

How do you handle a situation where a critical production system goes down, and you have conflicting opinions on how to resolve the issue?

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?

Why are you interested in working at Lozier, and how do you feel about touring our manufacturing facilities an

Medium · Behavioral & Cultural Fit

Why are you interested in working at Lozier, and how do you feel about touring our manufacturing facilities and learning our company history?

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?