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Five9 Software Engineer Interview Guide

Five9 Software Engineer interview guide: reported rounds, questions and how candidates describe the process.

The 60-second brief

As a Software Engineer at Five9, you are at the heart of transforming the customer experience industry. You will be responsible for building, maintaining, and scaling the cloud-based contact center solutions that power interactions for thousands of global enterprises. Your work directly impacts how businesses communicate with their customers, requiring a blend of high-performance engineering and a deep understanding of real-time communication systems. This role is both challenging and rewarding due to the massive scale and reliability requirements of the Five9 platform. You will engage with complex architectures, distributed systems, and modern web technologies to solve critical problems in latency, security, and integration.

This guide is scoped to a Software Engineer candidate at Five9.

Focus areas

  • Data Structures
  • Algorithms
  • System Design

The Five9 interview process

Five9 candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Recruiter Screen

Initial screening by a recruiter to assess candidate fit for the role.

What to demonstrate

  • Initial screening by a recruiter to assess candidate fit for the role
  • Depth in Data Structures

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 Evaluations

A series of technical interviews including live coding sessions and architectural discussions.

What to demonstrate

  • A series of technical interviews including live coding sessions and architectural discussions
  • Depth in Data Structures

How to prepare

  • Answer aloud and timed: Write a function to check if one word is a permutation of another.
  • Answer aloud and timed: Describe how you would implement an encryption algorithm for passwords.

Cultural Evaluations

Behavioral interviews with peers and management to assess cultural fit.

What to demonstrate

  • Behavioral interviews with peers and management to assess cultural fit
  • Depth in Data Structures

How to prepare

  • Answer aloud and timed: What are the time complexity implications of various operations on an array or linked list?
  • Answer aloud and timed: How would you design a system to book a conference room based on specific schedules and requests?

Where candidates lose points

Structure your answers

Use the STAR method for behavioral questions to keep your responses focused and impactful.

Think out loud

During coding sessions, the interviewer wants to hear your thought process. Silence is often less helpful than a well-articulated, albeit imperfect, approach.

Research the product

Familiarize yourself with what Five9 does in the cloud contact center space. Understanding our business adds depth to your answers.

Be ready for technical depth

Even if the interview starts with simple questions, be prepared to dive deep into why you chose a specific approach or technology.

Questions & practice

How do you handle asynchronous operations? Can you explain the event loop, promises, or closures?

Medium · Technical Foundations & Programming

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?

Describe how you would implement an encryption algorithm for passwords.

Medium · Technical Foundations & Programming

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?

What are the time complexity implications of various operations on an array or linked list?

Medium · Technical Foundations & Programming

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 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?

Explain why the owner filter ignores the listing index

Medium · composite index · query plans · sort elimination

The only index on resource is (tenant_id, status, updated_at DESC, resource_id DESC). A new endpoint returns one user's resources across all statuses, newest created first: WHERE tenant_id = $1 AND owner_user_id = $2 ORDER BY created_at DESC LIMIT 20. On a tenant with 2M rows it takes 900 ms and EXPLAIN shows a sort above a large scan. Explain precisely why the existing index cannot serve it, give the index that can, and state which of these the new index still will not help: owner_user_id alone across tenants; the same query ordered by updated_at. PostgreSQL 16.

Approach

  1. Separate the two jobs an index does. For filtering, a composite btree is seekable only on a left prefix, so with no predicate on status the scan can at best range over tenant_id and test owner_user_id per row; PostgreSQL 16 has no btree skip scan to jump the unconstrained column.
  2. For ordering, the index is sorted by (status, updated_at) within a tenant and not by created_at, so the LIMIT cannot stop early: every matching row is read and then sorted. That is the 'Sort Method: top-N heapsort' line, and it is why the plan reads 2M rows to answer with 20.
  3. Derive the replacement from the access path — equality, equality, then the ordering column: CREATE INDEX CONCURRENTLY ON resource (tenant_id, owner_user_id, created_at DESC). The scan seeks to the (tenant, owner) range and walks 20 entries in order, so the Sort node disappears along with the row-read.
  4. Treat INCLUDE (title, status) as conditional, not free. An index-only scan still visits the heap for any row whose page is not marked all-visible, so on a table taking 1.2k writes/second the win depends on autovacuum keeping the visibility map current, and the wider index costs more on every insert.

Follow-up

  • 90% of rows are status='active'. Would a partial index WHERE status = 'active' change your answer, and for which of the three queries?
  • A dashboard runs this for 40 owners in one page load. What changes about the design?

Explain the difference between an interface and an abstract class in your preferred language.

Medium · Technical Foundations & Programming

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?

Write a function to check if one word is a permutation of another.

Medium · Technical Foundations & Programming

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 would you design a system to book a conference room based on specific schedules and requests?

Medium · System Design & Problem Solving

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?

Given a piece of faulty code, how do you identify the bug and refactor it for better performance?

Medium · System Design & Problem Solving

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?

Can you explain how you would handle user authentication in a large-scale web application?

Medium · System Design & Problem Solving

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 do you approach designing a scalable API that handles high volumes of concurrent traffic?

Medium · System Design & Problem Solving

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 a time you had to optimize a piece of infrastructure; what were the trade-offs?

Medium · System Design & Problem Solving

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?

p99 jumped on one listing filter while p50 stayed flat

Medium · query planning · composite index · percentiles

After a release that added an owner_user_id filter to the resource listing, p99 rose from 90 ms to 1.9 s while p50 stayed at 40 ms. Traffic and row counts are unchanged. resource carries the index (tenant_id, status, updated_at DESC, resource_id DESC). The new query filters tenant_id and owner_user_id, orders by updated_at DESC, resource_id DESC, and takes 20 rows. On PostgreSQL, explain the shape of the regression, prove it from a query plan, and give the index you would add.

Approach

  1. Start from the shape. A flat p50 with a moved p99 means a subset of requests changed cost, not all of them, so the first job is naming the subset. Bucket the endpoint's latency by the tenant's row count; the natural hypothesis is that large tenants are a small share of requests and all of the tail.
  2. Get the plan for the new query on a large tenant with EXPLAIN (ANALYZE, BUFFERS). Expect an index scan over the tenant's range, a filter discarding most of it, then a Sort feeding the Limit, possibly reporting Sort Method: external merge Disk. Read actual rows on the scan node, not estimated.
  3. Explain why the existing index cannot serve it. A composite B-tree is seekable only as a left prefix, and with no equality predicate on status the scan cannot treat updated_at as an ordering, because rows in the tenant's range are ordered by status first. Everything matching must be read and sorted before LIMIT 20 can apply, so a tenant with 400,000 rows pays 400,000 rows to return 20.
  4. Add (tenant_id, owner_user_id, updated_at DESC, resource_id DESC). Equality on the first two columns leaves the index ordered by updated_at within that pair, so the plan becomes an index scan that stops after 20 rows with no Sort node. PostgreSQL can scan a B-tree backwards, so the DESC markers matter only if the two sort columns ever disagree in direction; keeping them explicit documents the order the keyset cursor depends on.

Follow-up

  • The endpoint paginates with OFFSET. What does page 500 cost with your index, and what does the keyset version cost?
  • How would you have caught this before release, given that a 10,000-row seed database produces the same plan shape at an unnoticeable cost?

Suggested practice checklist

Built from the rounds and topics Five9 candidates report.

Day 1 — Map the Five9 loop

  • Write out the reported sequence: Recruiter Screen, Technical Evaluations, Cultural Evaluations.
  • 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 Data Structures

  • Spend the session on Data Structures, which Five9 candidates report being tested on.
  • Write one worked example in Data Structures and time yourself on it.

Deliverable: One timed worked example in Data Structures.

Day 3 — Work Algorithms

  • Spend the session on Algorithms, which Five9 candidates report being tested on.
  • Write one worked example in Algorithms and time yourself on it.

Deliverable: One timed worked example in Algorithms.

Day 4 — Work System Design

  • Spend the session on System Design, which Five9 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: Technical Foundations & Programming

  • Answer aloud, timed: Explain the difference between an interface and an abstract class in your preferred language.
  • Answer aloud, timed: How do you handle asynchronous operations? Can you explain the event loop, promises, or closures?

Deliverable: Spoken answers to 2 reported Technical Foundations & Programming question(s), under time.

Day 6 — Answer out loud: System Design & Problem Solving

  • Answer aloud, timed: How would you design a system to book a conference room based on specific schedules and requests?
  • Answer aloud, timed: Given a piece of faulty code, how do you identify the bug and refactor it for better performance?

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

Day 7 — Answer out loud: Behavioral & Team Fit

  • Answer aloud, timed: Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder.
  • Answer aloud, timed: How do you handle situations where you disagree with a teammate’s technical approach?

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

Behavioural preparation

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

Tell me about a time you had to explain a complex technical concept to a non-technical stakeholder.

Medium · Behavioral & Team 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 handle situations where you disagree with a teammate’s technical approach?

Medium · Behavioral & Team 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 Five9, and what do you know about our cloud contact center space?

Medium · Behavioral & Team 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?

Describe a challenging project you led or contributed to and the specific hurdles you overcame.

Medium · Behavioral & Team 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?