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University of North Texas - Dallas Software Engineer Interview Guide

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

The 60-second brief

The Software Engineer at the University of North Texas - Dallas plays a vital role in building, maintaining, and optimizing the digital infrastructure that powers the university. This position is not just about writing code; it is about creating seamless experiences for students, faculty, and administrative staff. By developing robust applications, integrating enterprise systems, and managing critical databases, you directly contribute to the university's mission of fostering student success and community growth. In this role, you will work on a variety of internal systems, ranging from student information portals and registration tools to enterprise resource planning (ERP) systems and middleware integrations. The technical environment is highly collaborative, requiring close coordination with other IT professionals, database administrators, and academic stakeholders.

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

Focus areas

  • Java
  • JDBC (Java Database Connectivity)
  • SQL

The University of North Texas - Dallas interview process

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

Phone Screening

A brief, 15-minute conversation with the hiring manager focusing on your background and interest in the role.

What to demonstrate

  • A brief, 15-minute conversation with the hiring manager focusing on your background and interest in the role
  • Depth in Java

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 Interview

A comprehensive 30 to 60-minute remote interview with a panel discussing past projects and technical skills.

What to demonstrate

  • A comprehensive 30 to 60-minute remote interview with a panel discussing past projects and technical skills
  • Depth in Java

How to prepare

  • Answer aloud and timed: Explain the difference between an inner join and a left join, and provide a scenario where you would use each.
  • Answer aloud and timed: How do you handle database transaction management and prevent data corruption during concurrent writes?

Take-Home Assessment

Some candidates may receive a practical assessment to verify coding and database capabilities.

What to demonstrate

  • Some candidates may receive a practical assessment to verify coding and database capabilities
  • Depth in Java

How to prepare

  • Answer aloud and timed: Walk me through a recent software project you completed. What technologies did you choose and why?
  • Answer aloud and timed: Describe a time when you had to integrate two disparate systems. What challenges did you face, and how did you overcome them?

Where candidates lose points

Highlight your database skills

Relational databases are central to the university's IT infrastructure. Spend time reviewing database normalization, indexing, complex joins, and how to write efficient SQL queries.

Prepare your project stories

Be ready to discuss two or three past projects in deep detail. Focus on the technical challenges you faced, how you made architectural decisions, and the ultimate impact of your work.

Emphasize collaboration

The IT department works closely with various campus units. Highlight your experience gathering requirements from non-technical colleagues and translating them into functional software.

Questions & practice

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

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

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

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

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

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

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 optimize a complex SQL query that is running slowly in a production environment?

Medium · Core Backend & Database Technologies

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 difference between an inner join and a left join, and provide a scenario where you would use each.

Medium · Core Backend & Database Technologies

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?

Walk me through a recent software project you completed. What technologies did you choose and why?

Medium · Project Walkthroughs & Technical Experie

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 testing your code before it is deployed to a staging or production environment?

Medium · Project Walkthroughs & Technical Experie

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 University of North Texas - Dallas candidates report.

Day 1 — Map the University of North Texas - Dallas loop

  • Write out the reported sequence: Phone Screening, Technical Interview, Take-Home Assessment.
  • 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 Java

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

Deliverable: One timed worked example in Java.

Day 3 — Work JDBC (Java Database Connectivity)

  • Spend the session on JDBC (Java Database Connectivity), which University of North Texas - Dallas candidates report being tested on.
  • Write one worked example in JDBC (Java Database Connectivity) and time yourself on it.

Deliverable: One timed worked example in JDBC (Java Database Connectivity).

Day 4 — Work SQL

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

Deliverable: One timed worked example in SQL.

Day 5 — Answer out loud: Core Backend & Database Technologies

  • Answer aloud, timed: Are you able to code in Java, and can you describe your experience with JDBC?
  • Answer aloud, timed: How do you optimize a complex SQL query that is running slowly in a production environment?

Deliverable: Spoken answers to 2 reported Core Backend & Database Technologies question(s), under time.

Day 6 — Answer out loud: Project Walkthroughs & Technical Experience

  • Answer aloud, timed: Walk me through a recent software project you completed. What technologies did you choose and why?
  • Answer aloud, timed: Describe a time when you had to integrate two disparate systems. What challenges did you face, and how did you overcome them?

Deliverable: Spoken answers to 2 reported Project Walkthroughs & Technical Experience question(s), under time.

Day 7 — Answer out loud: Behavioral & Situational Alignment

  • Answer aloud, timed: Why are you interested in working as a Software Engineer for University of North Texas - Dallas?
  • Answer aloud, timed: Tell me about a time when you had to explain a complex technical issue to a non-technical stakeholder. How did you ensure they understood?

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

Behavioural preparation

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

Are you able to code in Java, and can you describe your experience with JDBC?

Medium · Core Backend & Database Technologies

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 database transaction management and prevent data corruption during concurrent writes?

Medium · Core Backend & Database Technologies

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 to integrate two disparate systems. What challenges did you face, and how did you

Medium · Project Walkthroughs & Technical Experie

Describe a time when you had to integrate two disparate systems. What challenges did you face, and how did you overcome them?

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?

What is your experience with version control systems like Git, and how do you manage merge conflicts in a team

Medium · Project Walkthroughs & Technical Experie

What is your experience with version control systems like Git, and how do you manage merge conflicts in a team setting?

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 as a Software Engineer for University of North Texas - Dallas?

Medium · Behavioral & Situational 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?

Tell me about a time when you had to explain a complex technical issue to a non-technical stakeholder. How did

Medium · Behavioral & Situational Alignment

Tell me about a time when you had to explain a complex technical issue to a non-technical stakeholder. How did you ensure they understood?

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 your tasks when you are assigned multiple urgent support tickets and development deadlin

Medium · Behavioral & Situational Alignment

How do you prioritize your tasks when you are assigned multiple urgent support tickets and development deadlines simultaneously?

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 teammate's technical approach. How did you resolve the disagre

Medium · Behavioral & Situational Alignment

Describe a situation where you disagreed with a teammate's technical approach. How did you resolve the disagreement and move forward?

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?