Interview guides · Texas Mutual Insurance

Texas Mutual Insurance Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at Texas Mutual Insurance, you are a pivotal contributor to the digital transformation of one of the state's most established insurance providers. You will work on systems that support critical business operations, ensuring that policyholders and injured workers receive the support they need through robust, scalable, and secure software solutions. Your work directly influences the efficiency of internal processes and the quality of digital services delivered to our clients. This role requires a balance of technical precision and a collaborative mindset. You will not only be writing code but also engaging with cross-functional teams to solve complex domain-specific problems. Whether you are building new features or maintaining core infrastructure, your contributions are essential to maintaining the company’s competitive edge in the insurance market.

This guide is scoped to a Software Engineer candidate at Texas Mutual Insurance.

Focus areas

Hands-on Coding (Practical Coding)
Behavioral Interviewing
Virtual Coding Challenge

The Texas Mutual Insurance interview process

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

Recruiter Screening

Initial contact with a recruiter to evaluate your background and fit for the role.

What to demonstrate

Initial contact with a recruiter to evaluate your background and fit for the role
Depth in Hands-on Coding (Practical Coding)

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 Assessments

Evaluation of your technical skills through coding challenges or assessments.

What to demonstrate

Evaluation of your technical skills through coding challenges or assessments
Depth in Hands-on Coding (Practical Coding)

How to prepare

Answer aloud and timed: What are the differences between the frameworks you have used in your previous roles?
Answer aloud and timed: How do you ensure the code you write is maintainable and scalable?

Panel Interviews

Interviews with multiple team members to assess both technical and behavioral fit.

What to demonstrate

Interviews with multiple team members to assess both technical and behavioral fit
Depth in Hands-on Coding (Practical Coding)

How to prepare

Answer aloud and timed: Can you explain how you would optimize a database query that is performing poorly?
Answer aloud and timed: Describe a time you had a disagreement with a team member; how did you resolve it?

Where candidates lose points

Research the company

Understand that Texas Mutual Insurance is in a phase of digital evolution; showing awareness of this can help you frame your answers.

Be honest about your skills

If you don't know an answer, it is better to explain how you would find the answer rather than guessing.

Prepare your own questions

The interview is a two-way street. Ask about the team's current technical hurdles and how they balance feature work with technical debt.

Questions & practice

Write a function to solve [common data structure problem].

Medium · Coding and Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
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?

Given a specific business logic requirement, how would you implement the validation layer?

Medium · Coding and Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
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?

Can you walk me through the logic of this code snippet and identify potential edge cases?

Medium · Coding and Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
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 test this component to ensure it meets the requirements?

Medium · Coding and Problem Solving

Approach

Restate the input: its shape, its size, and what is guaranteed about it.
Name the brute-force solution and its complexity before improving on it.
Choose the data structure from the access pattern, not from familiarity.
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?

Can you explain how you would optimize a database query that is performing poorly?

Medium · Technical and Domain Knowledge

Approach

Name the grain you start from and join outward from it.
Check whether any join is one-to-many before aggregating, or the sums inflate.
Say which index the query would use, and what makes it unusable.
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

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.
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.
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.
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?

Can you walk me through the architecture of a project you worked on recently?

Medium · Technical and Domain Knowledge

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
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?

What are the differences between the frameworks you have used in your previous roles?

Medium · Technical and Domain Knowledge

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
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 ensure the code you write is maintainable and scalable?

Medium · Technical and Domain Knowledge

Approach

Fix the scope first: who calls this, how often, and what they do when it fails.
Name the read and write paths separately; they rarely have the same bottleneck.
Choose a partition key and say what query it makes expensive.
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?

How would you structure the code for a simple API endpoint?

Medium · Coding and Problem Solving

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
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?

Listing latency scales with page size, not with filters

Easy · n+1 · latency · query batching

The tenant listing endpoint reads resource filtered by tenant_id and status, ordered by updated_at DESC, and returns each row plus the owner's display name from app_user and the actor of that resource's latest resource_revision. p99 is 55 ms at 10 rows per page and 1.4 s at 200. Database telemetry shows 401 statements per request, each under 1 ms, and nothing in the slow-query log. Diagnose the cause and give the fix, stating the statement count per request and the p99 you expect afterwards.

Approach

Read the counters before forming a theory. 401 statements for 200 rows is one driver query plus two per row, and sub-millisecond execution with an empty slow-query log rules out a bad plan. The time is round trips, which is why it is invisible in every per-query metric and scales with rows returned rather than with filter selectivity.
Name the two per-row statements from their normalised text: a single-row app_user lookup by user_id, and a resource_revision lookup by resource_id ordered by version DESC LIMIT 1. Confirm by dropping those two response fields and watching the statement count fall to one. That locates the calls in the serialisation layer, not the repository.
Check that the arithmetic accounts for the whole gap. Measure one round trip to the replica in isolation; 400 trips at roughly 3 ms of network plus 0.2 ms of execution is about 1.3 s on top of a 55 ms baseline, which matches. If the multiplication had fallen short, the N+1 would only be part of the story and you would keep looking.
Batch both lookups. Collect owner_user_ids and resource_ids from the driver query, then issue WHERE tenant_id = $1 AND user_id = ANY($2) for the users, and PostgreSQL's SELECT DISTINCT ON (resource_id) ... WHERE resource_id = ANY($2) ORDER BY resource_id, version DESC for the latest revision, which the UNIQUE (resource_id, version) index serves directly. On an engine without DISTINCT ON, use a lateral join or a row_number window. Three statements per request at any page size.

Follow-up

The page size is capped at 200 today. What breaks first if it is raised to 2,000, and is it still this bug?
How do you stop the next N+1 from reaching production, given that no individual query is slow and the endpoint's tests pass?

Suggested practice checklist

Built from the rounds and topics Texas Mutual Insurance candidates report.

Day 1 — Map the Texas Mutual Insurance loop

Write out the reported sequence: Recruiter Screening, Technical Assessments, Panel Interviews.
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 Hands-on Coding (Practical Coding)

Spend the session on Hands-on Coding (Practical Coding), which Texas Mutual Insurance candidates report being tested on.
Write one worked example in Hands-on Coding (Practical Coding) and time yourself on it.

Deliverable: One timed worked example in Hands-on Coding (Practical Coding).

Day 3 — Work Behavioral Interviewing

Spend the session on Behavioral Interviewing, which Texas Mutual Insurance candidates report being tested on.
Write one worked example in Behavioral Interviewing and time yourself on it.

Deliverable: One timed worked example in Behavioral Interviewing.

Day 4 — Work Virtual Coding Challenge

Spend the session on Virtual Coding Challenge, which Texas Mutual Insurance candidates report being tested on.
Write one worked example in Virtual Coding Challenge and time yourself on it.

Deliverable: One timed worked example in Virtual Coding Challenge.

Day 5 — Answer out loud: Technical and Domain Knowledge

Answer aloud, timed: Can you walk me through the architecture of a project you worked on recently?
Answer aloud, timed: How do you handle debugging when you encounter an issue in a production environment?

Deliverable: Spoken answers to 2 reported Technical and Domain Knowledge question(s), under time.

Day 6 — Answer out loud: Behavioral and Cultural Fit

Answer aloud, timed: Describe a time you had a disagreement with a team member; how did you resolve it?
Answer aloud, timed: Tell me about a time you had to learn a new technology quickly to finish a project.

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

Day 7 — Answer out loud: Coding and Problem Solving

Answer aloud, timed: Write a function to solve [common data structure problem].
Answer aloud, timed: How would you structure the code for a simple API endpoint?

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

Behavioural preparation

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

How do you handle debugging when you encounter an issue in a production environment?

Medium · Technical and Domain Knowledge

Approach

Pick a story where you made the decision, not one where you watched it.
State the situation in two sentences and spend the rest on the reasoning.
Give the blast radius: what could have broken, and what you measured.
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?