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Sam Houston State University Software Engineer Interview Guide

Sam Houston State University Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

A Software Engineer at Sam Houston State University (SHSU) plays a vital role in designing, developing, and maintaining the digital infrastructure that supports thousands of students, faculty, and administrative staff. In a higher education environment, software engineering is not just about writing code; it is about building reliable, accessible, and secure systems that power academic success, streamline enrollment, and facilitate cutting-edge research. You will work on a diverse array of projects, ranging from student information portals and enterprise resource planning (ERP) integrations to custom web applications that enhance the campus experience. The impact of this position is felt across the entire university community.

This guide is scoped to a Software Engineer candidate at Sam Houston State University.

Focus areas

  • Data Structures (DS)
  • Algorithms (Algo)
  • Coding Interviews

The Sam Houston State University interview process

Sam Houston State University candidates report 2 rounds over 2-4 weeks. The stages below are what candidates describe, not a published process.

Initial Screening

Recruiter reviews your qualifications, experience, and salary expectations.

What to demonstrate

  • Recruiter reviews your qualifications, experience, and salary expectations
  • Depth in Data Structures (DS)

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.

Panel Interview

Meeting with 3 to 4 team members to discuss technical background and behavioral scenarios.

What to demonstrate

  • Meeting with 3 to 4 team members to discuss technical background and behavioral scenarios
  • Depth in Data Structures (DS)

How to prepare

  • Answer aloud and timed: Write a function to reverse a string or check for palindromes, ensuring optimal time and space complexity.
  • Answer aloud and timed: Describe how you would identify and resolve a performance bottleneck in a recursive function.

Where candidates lose points

Prepare for the panel format

Speaking to a panel of 3 or 4 people can feel intimidating. Address the entire panel when answering, maintain good virtual eye contact, and speak clearly.

Emphasize security and compliance

In higher education, protecting student data (FERPA) and ensuring web accessibility (ADA compliance) are critical. Be sure to highlight your awareness of these standards during your interviews.

Highlight your integration experience

Much of the work at a university involves connecting different platforms. Share specific examples of how you have successfully integrated systems, designed APIs, or migrated data.

Standard university hiring processes can take longer than private sector roles due to administrative reviews

Maintain clear communication with your recruiter throughout the process.

Questions & practice

Explain the difference between an array and a linked list, and describe a scenario where you would prefer one

Medium · Data Structures & Algorithms

Explain the difference between an array and a linked list, and describe a scenario where you would prefer one over the other.

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 would you implement a search algorithm to find a specific student record in an unsorted dataset?

Medium · Data Structures & Algorithms

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?

Write a function to reverse a string or check for palindromes, ensuring optimal time and space complexity.

Medium · Data Structures & Algorithms

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?

Describe how you would identify and resolve a performance bottleneck in a recursive function.

Medium · Data Structures & Algorithms

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?

Describe your approach to designing a database schema for a course registration system, ensuring data normaliz

Medium · System Design & Integration

Describe your approach to designing a database schema for a course registration system, ensuring data normalization and integrity.

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

  1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
  2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
  3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
  4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

  • A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
  • How do you honour an erasure request while resource_revision.actor_user_id still references this table?

How would you design a secure API to transfer student enrollment data between the university’s ERP system and

Medium · System Design & Integration

How would you design a secure API to transfer student enrollment data between the university’s ERP system and a third-party learning management system?

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?

Say you need to integrate a legacy system with a modern web application. What strategies would you use to ensu

Medium · System Design & Integration

Say you need to integrate a legacy system with a modern web application. What strategies would you use to ensure seamless data flow?

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 your software designs comply with security regulations and data privacy laws (such as FERPA)

Medium · System Design & Integration

How do you ensure your software designs comply with security regulations and data privacy laws (such as FERPA)?

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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

  1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
  2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
  3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
  4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

  • The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
  • Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics Sam Houston State University candidates report.

Day 1 — Map the Sam Houston State University loop

  • Write out the reported sequence: Initial Screening, 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 2 reported rounds, with the weakest marked.

Day 2 — Work Data Structures (DS)

  • Spend the session on Data Structures (DS), which Sam Houston State University candidates report being tested on.
  • Write one worked example in Data Structures (DS) and time yourself on it.

Deliverable: One timed worked example in Data Structures (DS).

Day 3 — Work Algorithms (Algo)

  • Spend the session on Algorithms (Algo), which Sam Houston State University candidates report being tested on.
  • Write one worked example in Algorithms (Algo) and time yourself on it.

Deliverable: One timed worked example in Algorithms (Algo).

Day 4 — Work Coding Interviews

  • Spend the session on Coding Interviews, which Sam Houston State University candidates report being tested on.
  • Write one worked example in Coding Interviews and time yourself on it.

Deliverable: One timed worked example in Coding Interviews.

Day 5 — Answer out loud: Data Structures & Algorithms

  • Answer aloud, timed: Explain the difference between an array and a linked list, and describe a scenario where you would prefer one over the other.
  • Answer aloud, timed: How would you implement a search algorithm to find a specific student record in an unsorted dataset?

Deliverable: Spoken answers to 2 reported Data Structures & Algorithms question(s), under time.

Day 6 — Answer out loud: System Design & Integration

  • Answer aloud, timed: How would you design a secure API to transfer student enrollment data between the university’s ERP system and a third-party learning management system?
  • Answer aloud, timed: Describe your approach to designing a database schema for a course registration system, ensuring data normalization and integrity.

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

Day 7 — Answer out loud: Behavioral & Situational

  • Answer aloud, timed: Describe a time when you had a disagreement with a coworker or stakeholder about a technical implementation. How did you resolve it?
  • Answer aloud, timed: Tell me about a challenging project you worked on. What was your role, and how did you overcome the obstacles?

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

Behavioural preparation

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

What are the advantages of using a hash table, and how do you handle collisions?

Medium · Data Structures & Algorithms

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 session management and user authentication (such as Single Sign-On) across multiple campus a

Medium · System Design & Integration

How do you handle session management and user authentication (such as Single Sign-On) across multiple campus applications?

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 coworker or stakeholder about a technical implementation. H

Medium · Behavioral & Situational

Describe a time when you had a disagreement with a coworker or stakeholder 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 challenging project you worked on. What was your role, and how did you overcome the obstacles?

Medium · Behavioral & Situational

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 managing multiple urgent requests from different university departments?

Medium · Behavioral & Situational

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 & Situational

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 had to work with limited documentation to update or maintain an existing system.

Medium · Behavioral & Situational

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?