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Glassdoor Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at Glassdoor plays a central role in driving transparent, data-informed workplace insights for millions of job seekers and employers worldwide. Engineers at Glassdoor are responsible for building, scaling, and maintaining products that power company reviews, salary benchmark reports, job search algorithms, and workplace community tools. The work directly impacts how people discover opportunities and navigate their career decisions. In this role, you will work across high-scale distributed systems, robust backend APIs, and intuitive user-facing interfaces. Engineers regularly tackle technical challenges related to high-throughput data processing, search indexing, content aggregation, and complex relational and document-based data modeling.

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

Focus areas

  • Coding Interviews
  • Algorithmic Thinking
  • System Design

The Glassdoor interview process

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

Conversational Screening Call

Initial call with a talent acquisition partner to discuss your background, expectations, and interest in Glassdoor.

What to demonstrate

  • Initial call with a talent acquisition partner to discuss your background, expectations, and interest in Glassdoor
  • Depth in Coding Interviews

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 Screening

Includes a pair programming session or a targeted take-home practical exercise evaluating frontend or backend skills.

What to demonstrate

  • Includes a pair programming session or a targeted take-home practical exercise evaluating frontend or backend skills
  • Depth in Coding Interviews

How to prepare

  • Answer aloud and timed: Given a specific array or string processing problem, implement a solution and explain time complexity improvements over a naive approach.
  • Answer aloud and timed: Demonstrate how you would implement a custom hash map structure or binary search algorithm from scratch.

Full Interview Loop

Multiple targeted modules including algorithmic coding challenges, system design discussions, and behavioral interviews.

What to demonstrate

  • Multiple targeted modules including algorithmic coding challenges, system design discussions, and behavioral interviews
  • Depth in Coding Interviews

How to prepare

  • Answer aloud and timed: Solve a scheduling or calendar allocation problem using interval or array manipulation strategies.
  • Answer aloud and timed: Explain core Java OOP principles and walk through practical applications of design patterns like Observer, Singleton, and Factory.

Where candidates lose points

Structure Your Live Coding Approach

Before writing code during technical screens, spend the first few minutes clarifying requirements, writing out test input/output examples, and discussing your proposed algorithm with your interviewer.

Emphasize Code Quality and Readability

Write clean, modular code with clear variable naming. Interviewers evaluate your coding habits as if you were submitting a pull request to their production codebase.

Be Prepared for Architecture & Schema Discussions

Ensure you can talk through relational schema relationships, indexing strategies, and API contracts, as practical database knowledge is heavily evaluated.

Demonstrate Genuine Transparency

When answering behavioral questions, share authentic experiences, including technical challenges and missteps. Detail what you learned and how you adapted your engineering approach.

Ask Thoughtful Team Questions

Dedicate time at the end of each interview round to ask insightful questions about engineering culture, deployment frequency, system scale, and team organization.

Questions & practice

Implement a solution using a hash set to filter duplicate entries in a stream and loop through input efficient

Medium · Algorithmic Problem Solving & Live Codin

Implement a solution using a hash set to filter duplicate entries in a stream and loop through input efficiently.

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 an optimal algorithm using a priority queue or recursion to filter, aggregate, and sort structured data.

Medium · Algorithmic Problem Solving & Live Codin

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?

Given a specific array or string processing problem, implement a solution and explain time complexity improvem

Medium · Algorithmic Problem Solving & Live Codin

Given a specific array or string processing problem, implement a solution and explain time complexity improvements over a naive approach.

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?

Demonstrate how you would implement a custom hash map structure or binary search algorithm from scratch.

Medium · Algorithmic Problem Solving & Live Codin

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?

Solve a scheduling or calendar allocation problem using interval or array manipulation strategies.

Medium · Algorithmic Problem Solving & Live Codin

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 do multithreading, asynchronous I/O, and process parallelism differ in Python, and when should each be use

Medium · Object-Oriented Design & Technical Funda

How do multithreading, asynchronous I/O, and process parallelism differ in Python, and when should each be used?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

What are the core execution differences between event-driven platforms like Node.js and multithreaded runtime

Medium · Object-Oriented Design & Technical Funda

What are the core execution differences between event-driven platforms like Node.js and multithreaded runtime environments?

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

Explain memory management principles and garbage collection considerations when processing large data payloads

Medium · Object-Oriented Design & Technical Funda

Explain memory management principles and garbage collection considerations when processing large data payloads in memory.

Approach

  1. Say what the runtime actually does before reasoning about the code.
  2. Name what is shared across threads and what owns each piece of state.
  3. Identify the window where an invariant is briefly untrue.
  4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

  • What happens if two callers reach this at the same time?
  • Where could this allocate more than you expect?

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

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

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?

Explain core Java OOP principles and walk through practical applications of design patterns like Observer, Sin

Medium · Object-Oriented Design & Technical Funda

Explain core Java OOP principles and walk through practical applications of design patterns like Observer, Singleton, and Factory.

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 construct reliable unit and integration tests for critical backend modules?

Medium · Object-Oriented Design & Technical Funda

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 would you design a database schema and split data across multiple tables or shards using foreign keys?

Medium · System Architecture & Database 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?

Explain the technical trade-offs between relational databases like MySQL and document-based NoSQL stores for h

Medium · System Architecture & Database Design

Explain the technical trade-offs between relational databases like MySQL and document-based NoSQL stores for high-traffic web applications.

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?

How would you design an online rating and review system capable of handling high write loads and real-time agg

Medium · System Architecture & Database Design

How would you design an online rating and review system capable of handling high write loads and real-time aggregations?

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?

Describe how you structure RESTful APIs or modern service endpoints to handle caching, pagination, and failure

Medium · System Architecture & Database Design

Describe how you structure RESTful APIs or modern service endpoints to handle caching, pagination, and failure recovery.

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?

Walk through a strategy for managing database migrations without causing service downtime on production platfo

Medium · System Architecture & Database Design

Walk through a strategy for managing database migrations without causing service downtime on production platforms.

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?

Read latency spikes on a sixty-second sawtooth

Medium · cache stampede · single-flight · ttl

The cached listing read path serves about 14k reads/second at an 85% hit rate. p99 sits at 35 ms for 57 seconds, jumps to 900 ms for 3, and repeats. During each spike the primary shows several hundred identical listing queries starting within the same millisecond, all carrying one large tenant's id. Cache entries use a 60-second TTL. Give the mechanism, the ordered checks, the fix, and the correctness hazard your fix must not introduce.

Approach

  1. Match the period to a configured number before theorising about load. A spike every 60 seconds against a 60-second TTL is an entry expiring, and you confirm it by correlating spike timestamps with the entry's write time rather than with the traffic curve. If the period had matched a cron or a GC interval instead, this is a different investigation.
  2. Establish the concurrency of the miss. Several hundred identical queries in one millisecond means the miss path has no coalescing: every request that arrives between expiry and repopulation recomputes. The herd size is that key's arrival rate times its recompute time, so at 1.2k reads/second for the hot key and a 250 ms recompute you expect about 300 concurrent misses, which matches what is observed.
  3. Add single-flight on the miss path so one caller per key recomputes under a short-lived lock while the rest wait for its result. Prefer stale-while-revalidate where the read tolerates it: return the expired value immediately and refresh asynchronously, which removes the latency spike rather than serialising it into a queue of waiters.
  4. De-synchronise the keys. Write TTLs with jitter, for example 60 seconds plus or minus 10%, so a deploy or a mass invalidation does not align every key on the same second and turn a per-key herd into a fleet-wide one.

Follow-up

  • The same sawtooth appears on a key that is invalidated on write rather than expired. Is that the same bug?
  • How does your answer change if the recompute takes 4 seconds instead of 250 ms?

Suggested practice checklist

Built from the rounds and topics Glassdoor candidates report.

Day 1 — Map the Glassdoor loop

  • Write out the reported sequence: Conversational Screening Call, Technical Screening, Full Interview Loop.
  • 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 Coding Interviews

  • Spend the session on Coding Interviews, which Glassdoor 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 3 — Work Algorithmic Thinking

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

Deliverable: One timed worked example in Algorithmic Thinking.

Day 4 — Work System Design

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

Deliverable: One timed worked example in System Design.

Day 5 — Answer out loud: Algorithmic Problem Solving & Live Coding

  • Answer aloud, timed: Implement a solution using a hash set to filter duplicate entries in a stream and loop through input efficiently.
  • Answer aloud, timed: Write an optimal algorithm using a priority queue or recursion to filter, aggregate, and sort structured data.

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

Day 6 — Answer out loud: Object-Oriented Design & Technical Fundamentals

  • Answer aloud, timed: Explain core Java OOP principles and walk through practical applications of design patterns like Observer, Singleton, and Factory.
  • Answer aloud, timed: How do multithreading, asynchronous I/O, and process parallelism differ in Python, and when should each be used?

Deliverable: Spoken answers to 2 reported Object-Oriented Design & Technical Fundamentals question(s), under time.

Day 7 — Answer out loud: System Architecture & Database Design

  • Answer aloud, timed: How would you design a database schema and split data across multiple tables or shards using foreign keys?
  • Answer aloud, timed: Explain the technical trade-offs between relational databases like MySQL and document-based NoSQL stores for high-traffic web applications.

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

Behavioural preparation

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

Describe a challenging engineering project you worked on, detailing your specific technical contributions and

Medium · Behavioral & Cultural Fit

Describe a challenging engineering project you worked on, detailing your specific technical contributions and key learnings.

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 competing project deadlines, technical debt, and sudden production bugs?

Medium · Behavioral & Cultural 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?

Why do you want to work at Glassdoor, and how do you embody transparency and open communication in your day-to

Medium · Behavioral & Cultural Fit

Why do you want to work at Glassdoor, and how do you embody transparency and open communication in your day-to-day work?

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 you had to collaborate closely with a Product Manager, SDET, or Director of Engineering t

Medium · Behavioral & Cultural Fit

Tell me about a time you had to collaborate closely with a Product Manager, SDET, or Director of Engineering to ship a feature.

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 a technical project did not go as planned and how you communicated the issues to yo

Medium · Behavioral & Cultural Fit

Describe a situation where a technical project did not go as planned and how you communicated the issues to your team.

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?