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University of North Texas - System Administration Software Engineer Interview Guide

University of North Texas - System Administration Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at the University of North Texas - System Administration plays a pivotal role in designing, developing, and maintaining the enterprise-level software infrastructure that supports multiple campuses, thousands of faculty members, and tens of thousands of students. Unlike typical commercial product-focused roles, engineers within the UNT System focus on building highly reliable, secure, and integrated administrative systems, student portals, and data pipelines. Your work directly impacts the efficiency of university operations, from student enrollment systems to financial reporting and human resources portals. The engineering team is responsible for bridging legacy systems with modern cloud solutions, ensuring seamless data flow across diverse university departments.

This guide is scoped to a Software Engineer candidate at University of North Texas - System Administration.

Focus areas

  • Data Structures & Algorithms (DSA)
  • Medical Coding Systems (ICD-10, CPT)
  • System Design

The University of North Texas - System Administration interview process

University of North Texas - System Administration candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Application Screening

Initial review of applications to assess qualifications and fit for the role.

What to demonstrate

  • Initial review of applications to assess qualifications and fit for the role
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Describe a time when you had a disagreement with a coworker about a technical implementation. How did you resolve it?
  • Answer aloud and timed: Tell me about a time you had to work under a tight deadline with incomplete requirements.

HR Phone Call

Brief call with HR or recruiter to verify qualifications, salary expectations, and role alignment.

What to demonstrate

  • Brief call with HR or recruiter to verify qualifications, salary expectations, and role alignment
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: How do you handle receiving critical feedback on your code or system designs from a peer?
  • Answer aloud and timed: Describe a situation where you had to explain a complex technical concept to a non-technical stakeholder.

Panel Interview

Formal interview conducted virtually with a panel of 3-4 team members focusing on behavioral, technical, and system integration questions.

What to demonstrate

  • Formal interview conducted virtually with a panel of 3-4 team members focusing on behavioral, technical, and system integration questions
  • Depth in Data Structures & Algorithms (DSA)

How to prepare

  • Answer aloud and timed: Explain your experience with API design and integrating third-party software with legacy enterprise systems.
  • Answer aloud and timed: How do you ensure data integrity and security when moving sensitive student or financial data between systems?

Where candidates lose points

Do not be discouraged by silence

During panel interviews, panel members are required to take detailed notes on your answers. Do not be alarmed if there are brief pauses or if interviewers seem quiet while you are speaking; they are simply documenting your responses to ensure a fair evaluation.

Emphasize security and compliance

In a university system, data privacy (FERPA) and security are critical. Whenever you discuss system design or data handling, explicitly mention how you ensure security, validation, and compliance.

Never downplay the importance of documentation or compliance

In a public sector role, following established processes and maintaining clear documentation is just as important as writing the code itself.

Questions & practice

Describe your familiarity with version control systems like Git and your preferred branching strategy for team

Medium · Coding & Problem Solving

Describe your familiarity with version control systems like Git and your preferred branching strategy for team projects.

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?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

  1. One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
  2. Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
  3. Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
  4. If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

  • The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
  • How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

  1. Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
  2. Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
  3. Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
  4. Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

  • The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
  • Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

  1. Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
  2. Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
  3. Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
  4. Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

  • Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
  • Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

Explain your experience with API design and integrating third-party software with legacy enterprise systems.

Medium · Technical & System Integration

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?

How do you ensure data integrity and security when moving sensitive student or financial data between systems?

Medium · Technical & System Integration

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?

What is your approach to reviewing technical codes, standards, and regulations to ensure compliance during a s

Medium · Technical & System Integration

What is your approach to reviewing technical codes, standards, and regulations to ensure compliance during a system upgrade?

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?

What is your process for debugging a critical production issue when there is limited logging available?

Medium · Coding & Problem Solving

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

  • What would you look at first, and what would it rule out?
  • How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics University of North Texas - System Administration candidates report.

Day 1 — Map the University of North Texas - System Administration loop

  • Write out the reported sequence: Application Screening, HR Phone Call, Panel Interview.
  • 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 Data Structures & Algorithms (DSA)

  • Spend the session on Data Structures & Algorithms (DSA), which University of North Texas - System Administration 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 3 — Work Medical Coding Systems (ICD-10, CPT)

  • Spend the session on Medical Coding Systems (ICD-10, CPT), which University of North Texas - System Administration candidates report being tested on.
  • Write one worked example in Medical Coding Systems (ICD-10, CPT) and time yourself on it.

Deliverable: One timed worked example in Medical Coding Systems (ICD-10, CPT).

Day 4 — Work System Design

  • Spend the session on System Design, which University of North Texas - System Administration candidates report being tested on.
  • Write one worked example in System Design and time yourself on it.

Deliverable: One timed worked example in System Design.

Day 5 — Answer out loud: Behavioral & Team Collaboration

  • Answer aloud, timed: Describe a time when you had a disagreement with a coworker about a technical implementation. How did you resolve it?
  • Answer aloud, timed: Tell me about a time you had to work under a tight deadline with incomplete requirements.

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

Day 6 — Answer out loud: Technical & System Integration

  • Answer aloud, timed: Explain your experience with API design and integrating third-party software with legacy enterprise systems.
  • Answer aloud, timed: How do you ensure data integrity and security when moving sensitive student or financial data between systems?

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

Day 7 — Answer out loud: Coding & Problem Solving

  • Answer aloud, timed: Walk us through your experience with object-oriented programming (OOP) concepts and how you apply them in your daily work.
  • Answer aloud, timed: What is your process for debugging a critical production issue when there is limited logging available?

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

Behavioural preparation

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

Describe a time when you had a disagreement with a coworker about a technical implementation. How did you reso

Medium · Behavioral & Team Collaboration

Describe a time when you had a disagreement with a coworker about a technical implementation. How did you resolve 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?

Tell me about a time you had to work under a tight deadline with incomplete requirements.

Medium · Behavioral & Team Collaboration

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 receiving critical feedback on your code or system designs from a peer?

Medium · Behavioral & Team Collaboration

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 had to explain a complex technical concept to a non-technical stakeholder.

Medium · Behavioral & Team Collaboration

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 your experience with database design, specifically writing and optimizing complex SQL queries.

Medium · Technical & System Integration

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?

Walk us through your experience with object-oriented programming (OOP) concepts and how you apply them in your

Medium · Coding & Problem Solving

Walk us through your experience with object-oriented programming (OOP) concepts and how you apply them in your daily work.

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?