Interview guides · COTA

COTA Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at COTA plays a pivotal role in bridging the gap between complex clinical oncology data and life-saving cancer research. At its core, COTA is a healthcare technology company dedicated to organizing and structuring real-world clinical data to power cancer care insights. As an engineer here, you are not just writing code; you are architecting the platforms, pipelines, and tools that ingest, process, and analyze massive volumes of unstructured medical records, transforming them into actionable datasets for researchers, clinicians, and life science partners. This position is highly critical to COTA's core products, which rely heavily on data integrity, scalability, and security.

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

Focus areas

System Design (Architecture)
Big-O Time/Space Complexity
Whiteboard/System Explanation

The COTA interview process

COTA candidates report 4 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Screening Call

A conversational call with the recruiter or VP of Engineering focusing on your background, career goals, and alignment with COTA's mission.

What to demonstrate

A conversational call with the recruiter or VP of Engineering focusing on your background, career goals, and alignment with COTA's mission
Depth in System Design (Architecture)

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

Includes a hands-on coding homework assignment or a pair-programming session with a senior engineer to evaluate coding practices and problem-solving skills.

What to demonstrate

Includes a hands-on coding homework assignment or a pair-programming session with a senior engineer to evaluate coding practices and problem-solving skills
Depth in System Design (Architecture)

How to prepare

Answer aloud and timed: What strategies would you use to ensure high availability and data consistency in a distributed system?
Answer aloud and timed: How do you approach database schema design when dealing with evolving, semi-structured clinical data?

Onsite / Virtual Loop

A series of deeper interviews focusing on system design, technical architecture, and cross-functional collaboration with team engineers and stakeholders.

What to demonstrate

A series of deeper interviews focusing on system design, technical architecture, and cross-functional collaboration with team engineers and stakeholders
Depth in System Design (Architecture)

How to prepare

Answer aloud and timed: Write a function to solve a specific algorithmic problem, and explain its time and space complexity.
Answer aloud and timed: How do you optimize an algorithm that is experiencing bottleneck issues due to high Big-O complexity?

Final Leadership Review

A wrap-up conversation with senior technical leadership to recap prior discussions and align on final expectations.

What to demonstrate

A wrap-up conversation with senior technical leadership to recap prior discussions and align on final expectations
Depth in System Design (Architecture)

How to prepare

Answer aloud and timed: Walk through a pair-programming exercise to refactor a piece of legacy code for better readability and performance.
Answer aloud and timed: How do you handle edge cases and error boundaries in a multi-threaded execution environment?

Where candidates lose points

Master your resume architecture

Be ready to draw out the architecture of any system listed on your resume. If you built a microservice or designed a database schema, know the exact trade-offs of those decisions.

Understand COTA's mission

Before your first call, spend time researching how real-world data (RWD) is used in oncology. Being able to connect your engineering skills to this mission is a huge differentiator.

Avoid focusing solely on coding syntax during your technical rounds

COTA values engineers who can think at a system level and explain the business impact of their technical choices.

Questions & practice

Write a function to solve a specific algorithmic problem, and explain its time and space complexity.

Medium · Coding & 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 do you optimize an algorithm that is experiencing bottleneck issues due to high Big-O complexity?

Medium · Coding & 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?

Walk through a pair-programming exercise to refactor a piece of legacy code for better readability and perform

Medium · Coding & Problem Solving

Walk through a pair-programming exercise to refactor a piece of legacy code for better readability and performance.

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 do you approach database schema design when dealing with evolving, semi-structured clinical data?

Medium · System Design & Architecture

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?

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

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.
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.
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.
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?

Describe a complex system you have architected recently. Use the whiteboard to map out the components, data fl

Medium · System Design & Architecture

Describe a complex system you have architected recently. Use the whiteboard to map out the components, data flow, and trade-offs.

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 design a data ingestion pipeline that processes large, unstructured files from multiple external

Medium · System Design & Architecture

How would you design a data ingestion pipeline that processes large, unstructured files from multiple external sources?

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 strategies would you use to ensure high availability and data consistency in a distributed system?

Medium · System Design & Architecture

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?

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 COTA candidates report.

Day 1 — Map the COTA loop

Write out the reported sequence: Initial Screening Call, Technical Assessments, Onsite / Virtual Loop, Final Leadership Review.
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 4 reported rounds, with the weakest marked.

Day 2 — Work System Design (Architecture)

Spend the session on System Design (Architecture), which COTA candidates report being tested on.
Write one worked example in System Design (Architecture) and time yourself on it.

Deliverable: One timed worked example in System Design (Architecture).

Day 3 — Work Big-O Time/Space Complexity

Spend the session on Big-O Time/Space Complexity, which COTA candidates report being tested on.
Write one worked example in Big-O Time/Space Complexity and time yourself on it.

Deliverable: One timed worked example in Big-O Time/Space Complexity.

Day 4 — Work Whiteboard/System Explanation

Spend the session on Whiteboard/System Explanation, which COTA candidates report being tested on.
Write one worked example in Whiteboard/System Explanation and time yourself on it.

Deliverable: One timed worked example in Whiteboard/System Explanation.

Day 5 — Answer out loud: System Design & Architecture

Answer aloud, timed: Describe a complex system you have architected recently. Use the whiteboard to map out the components, data flow, and trade-offs.
Answer aloud, timed: How would you design a data ingestion pipeline that processes large, unstructured files from multiple external sources?

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

Day 6 — Answer out loud: Coding & Problem Solving

Answer aloud, timed: Write a function to solve a specific algorithmic problem, and explain its time and space complexity.
Answer aloud, timed: How do you optimize an algorithm that is experiencing bottleneck issues due to high Big-O complexity?

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

Day 7 — Answer out loud: Behavioral & Culture Fit

Answer aloud, timed: Why are you interested in working in the healthcare technology sector, and what draws you specifically to COTA's mission?
Answer aloud, timed: Describe a time when you had a technical disagreement with a senior engineer or architect. How did you resolve it?

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

Behavioural preparation

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

How do you handle edge cases and error boundaries in a multi-threaded execution environment?

Medium · Coding & Problem Solving

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?

Why are you interested in working in the healthcare technology sector, and what draws you specifically to COTA

Medium · Behavioral & Culture Fit

Why are you interested in working in the healthcare technology sector, and what draws you specifically to COTA's mission?

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?

Describe a time when you had a technical disagreement with a senior engineer or architect. How did you resolve

Medium · Behavioral & Culture Fit

Describe a time when you had a technical disagreement with a senior engineer or architect. How did you resolve it?

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?

Walk me through a time when you had to deliver a project under a tight deadline with incomplete requirements.

Medium · Behavioral & Culture Fit

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?

How do you keep up with new technologies, and how do you decide when to introduce a new tool to your team's st

Medium · Behavioral & Culture Fit

How do you keep up with new technologies, and how do you decide when to introduce a new tool to your team's stack?

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?