LevelUp Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at LevelUp, you are at the center of a platform designed to bridge the gap between businesses and their customers through seamless digital integration. You will contribute to a high-scale environment where your code directly impacts the reliability and functionality of payment processing, loyalty programs, and merchant-facing tools. This role requires a balance of technical precision and product-minded thinking, as you will often work on problems that require both clean architecture and an intuitive understanding of the user experience. Success in this position means more than just writing functional code; it requires a commitment to maintainable, test-driven development.

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

Focus areas

• Algorithmic Problem Solving
• RESTful APIs
• High-Level System Design

The LevelUp interview process

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

Recruiter Screen

Initial discussion with a recruiter to cover logistics and your background.

What to demonstrate

• Initial discussion with a recruiter to cover logistics and your background
• Depth in Algorithmic Problem Solving

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

A series of assessments including take-home coding challenges and virtual coding sessions.

What to demonstrate

• A series of assessments including take-home coding challenges and virtual coding sessions
• Depth in Algorithmic Problem Solving

How to prepare

• Answer aloud and timed: Write a function that checks if all alphabet letters are present in a phrase.
• Answer aloud and timed: Implement a mechanism for counting instantiated classes.

Onsite Visit

Multiple back-to-back interviews with members of the engineering team.

What to demonstrate

• Multiple back-to-back interviews with members of the engineering team
• Depth in Algorithmic Problem Solving

How to prepare

• Answer aloud and timed: Read through existing production code and explain the design.
• Answer aloud and timed: Write a RESTful app that can accept and parse log data.

Where candidates lose points

Show your work

When solving problems, explain your trade-offs. Why did you choose one data structure over another?

Be proactive

If the interviewer is quiet, keep them updated on your progress. It demonstrates confidence and collaboration.

Research the product

Spend time understanding how LevelUp functions from a user's perspective. It will make your behavioral answers much more grounded.

Clarify requirements

Never start coding until you are sure you understand the problem. Asking clarifying questions is a sign of a senior-level engineer.

Questions & practice

Write a function to determine if a string is a palindrome.

Medium · Coding and 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?

Find the largest palindrome from the product of two 3-digit numbers.

Medium · Coding and 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 that checks if all alphabet letters are present in a phrase.

Medium · Coding and 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?

Implement a mechanism for counting instantiated classes.

Medium · Coding and 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?

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?

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?

Read through existing production code and explain the design.

Medium · Technical and System Design

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?

Write a RESTful app that can accept and parse log data.

Medium · Technical and System Design

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?

Design a system or endpoint based on given technical requirements.

Medium · Technical and System Design

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?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

1. Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
2. Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
3. Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
4. Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

• The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
• You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics LevelUp candidates report.

Day 1 — Map the LevelUp loop

• Write out the reported sequence: Recruiter Screen, Technical Assessments, Onsite Visit.
• 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 Algorithmic Problem Solving

• Spend the session on Algorithmic Problem Solving, which LevelUp candidates report being tested on.
• Write one worked example in Algorithmic Problem Solving and time yourself on it.

Deliverable: One timed worked example in Algorithmic Problem Solving.

Day 3 — Work RESTful APIs

• Spend the session on RESTful APIs, which LevelUp candidates report being tested on.
• Write one worked example in RESTful APIs and time yourself on it.

Deliverable: One timed worked example in RESTful APIs.

Day 4 — Work High-Level System Design

• Spend the session on High-Level System Design, which LevelUp candidates report being tested on.
• Write one worked example in High-Level System Design and time yourself on it.

Deliverable: One timed worked example in High-Level System Design.

Day 5 — Answer out loud: Coding and Algorithms

• Answer aloud, timed: Write a function to determine if a string is a palindrome.
• Answer aloud, timed: Find the largest palindrome from the product of two 3-digit numbers.

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

Day 6 — Answer out loud: Technical and System Design

• Answer aloud, timed: Read through existing production code and explain the design.
• Answer aloud, timed: Write a RESTful app that can accept and parse log data.

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

Day 7 — Answer out loud: Behavioral and Role Fit

• Answer aloud, timed: Why are you interested in this position at LevelUp?
• Answer aloud, timed: Tell me about your previous programming experience.

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

Behavioural preparation

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

Why are you interested in this position at LevelUp?

Medium · Behavioral and Role Fit

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 your previous programming experience.

Medium · Behavioral and Role Fit

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 approach testing your own code?

Medium · Behavioral and Role Fit

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?