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Castlight Software Engineer Interview Guide

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

The 60-second brief

Castlight hires Software Engineers; this guide collects what candidates report about the process.

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

Focus areas

  • DSA (Data Structures and Algorithms)
  • Coding interviews (whiteboard/live coding)
  • System Design

The Castlight interview process

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

Recruiter Pre-Screen

Initial conversation focused on background, career objectives, and technical alignment.

What to demonstrate

  • Initial conversation focused on background, career objectives, and technical alignment
  • Depth in DSA (Data Structures and Algorithms)

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

One to two telephonic or virtual technical screens with an engineering manager or senior engineer.

What to demonstrate

  • One to two telephonic or virtual technical screens with an engineering manager or senior engineer
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: Demonstrate how to structure code cleanly during a live whiteboard or shared-screen exercise, incorporating proper error handling.
  • Answer aloud and timed: Write unit tests for a newly authored algorithm during a live coding session to verify edge-case coverage.

Onsite/Panel Interview

Intensive onsite or multi-session virtual panel interview consisting of technical deep dives.

What to demonstrate

  • Intensive onsite or multi-session virtual panel interview consisting of technical deep dives
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Answer aloud and timed: Given a set of relationships or dependencies, design a graph-based solution to find optimal paths or groupings.
  • Answer aloud and timed: Design a healthcare benefit navigation microservice capable of serving high-concurrency requests while maintaining low latency.

Hiring Manager Round

Dedicated round focused on team fit, project history, and behavioral alignment.

What to demonstrate

  • Dedicated round focused on team fit, project history, and behavioral alignment
  • Depth in DSA (Data Structures and Algorithms)

How to prepare

  • Prepare two projects you led end to end, each with the decision you owned and what it cost.
  • Have three questions about the team's roadmap and how success is measured in the first six months.

Where candidates lose points

Structure your code with testing in mind

During live coding exercises, write clean, modular functions and explicitly write or talk through unit test cases for boundary conditions.

Be ready to discuss design patterns explicitly

Be prepared to explain why and when you would implement patterns like Singleton, Factory, or Observer in your project code.

Do not jump straight into whiteboard or live coding without verifying constraints

Always outline your solution and discuss time/space complexities first.

Maintain clear compensation transparency

Ensure your salary expectations are communicated clearly and aligned with talent acquisition early in the process to avoid late-stage misalignment.

Questions & practice

Implement an efficient algorithm to solve a multi-dimensional grid or matrix challenge, such as designing the

Medium · Data Structures, Algorithms & Live Codin

Implement an efficient algorithm to solve a multi-dimensional grid or matrix challenge, such as designing the core mechanics for a Minesweeper game.

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 array, string, and graph traversal problems, explaining time and space complexity at every step.

Medium · Data Structures, Algorithms & 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?

Demonstrate how to structure code cleanly during a live whiteboard or shared-screen exercise, incorporating pr

Medium · Data Structures, Algorithms & Live Codin

Demonstrate how to structure code cleanly during a live whiteboard or shared-screen exercise, incorporating proper error handling.

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 unit tests for a newly authored algorithm during a live coding session to verify edge-case coverage.

Medium · Data Structures, Algorithms & 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?

Describe a project where you were given ambiguous or incomplete technical requirements and explain how you dro

Medium · Behavioral & Work Style Scenarios

Describe a project where you were given ambiguous or incomplete technical requirements and explain how you drove it to clarity.

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?

Describe strategies for data partitioning, indexing, and query optimization when handling large relational dat

Medium · System Design & API Architecture

Describe strategies for data partitioning, indexing, and query optimization when handling large relational datasets in SQL.

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

Compare different database indexing techniques and write complex SQL queries involving multiple joins and aggr

Medium · Object-Oriented Design & Technical Funda

Compare different database indexing techniques and write complex SQL queries involving multiple joins and aggregations.

Approach

  1. Name the grain you start from and join outward from it.
  2. Check whether any join is one-to-many before aggregating, or the sums inflate.
  3. Say which index the query would use, and what makes it unusable.
  4. 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?

Given a set of relationships or dependencies, design a graph-based solution to find optimal paths or groupings

Medium · Data Structures, Algorithms & Live Codin

Given a set of relationships or dependencies, design a graph-based solution to find optimal paths or groupings.

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 healthcare benefit navigation microservice capable of serving high-concurrency requests while maintai

Medium · System Design & API Architecture

Design a healthcare benefit navigation microservice capable of serving high-concurrency requests while maintaining low latency.

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?

Walk through the design and contract definition of modern REST APIs, specifying request/response payloads, sta

Medium · System Design & API Architecture

Walk through the design and contract definition of modern REST APIs, specifying request/response payloads, status codes, and error formats.

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 how you would decouple heavy data processing jobs from client-facing services using queueing or event-

Medium · System Design & API Architecture

Explain how you would decouple heavy data processing jobs from client-facing services using queueing or event-driven mechanisms.

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 do you ensure high availability, data consistency, and HIPAA compliance when architecting cloud-hosted ser

Medium · System Design & API Architecture

How do you ensure high availability, data consistency, and HIPAA compliance when architecting cloud-hosted services?

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 fundamental Java object-oriented concepts and demonstrate how to apply design patterns such as Singlet

Medium · Object-Oriented Design & Technical Funda

Explain fundamental Java object-oriented concepts and demonstrate how to apply design patterns such as Singleton, Observer, Factory, and State.

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?

Discuss backend development best practices using Spring and Spring Boot, including dependency injection and tr

Medium · Object-Oriented Design & Technical Funda

Discuss backend development best practices using Spring and Spring Boot, including dependency injection and transactional management.

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 approach front-end application architecture and state management when building modular components i

Medium · Object-Oriented Design & Technical Funda

How do you approach front-end application architecture and state management when building modular components in Angular?

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 life cycle of an enterprise application request from the front-end interface through API gateways

Medium · Object-Oriented Design & Technical Funda

Explain the life cycle of an enterprise application request from the front-end interface through API gateways down to the persistence layer.

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 past technical initiative, highlighting your individual contributions, architecture decisions,

Medium · Behavioral & Work Style Scenarios

Walk through a past technical initiative, highlighting your individual contributions, architecture decisions, and code quality standards.

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?

Every query on one table stalls for forty seconds mid-deploy

Medium · lock contention · ddl · migrations

During a release on PostgreSQL, every query touching resource times out for about 40 seconds and then recovers with no intervention. The release ran one migration, ALTER TABLE resource ADD COLUMN archived_reason TEXT, and the migration log shows it completing in 6 ms. Unrelated tables showed no change in error rate. Explain how a 6 ms statement caused a 40-second stall, give the ordered checks you would run on a live system to confirm it, and give the migration procedure that prevents a repeat.

Approach

  1. Separate the statement's duration from the lock's duration. ADD COLUMN with no default is a catalogue-only change and genuinely runs in milliseconds, but it requires ACCESS EXCLUSIVE, and it cannot acquire that until every transaction already touching the table has finished.
  2. Account for the queueing, which is the part that surprises people. A lock request that is waiting blocks later requests for conflicting modes behind it rather than letting them overtake, so one long-open transaction holds the DDL and the DDL holds all the traffic. The stall length is set by the longest open transaction, not by the size of the change.
  3. Confirm on a live system in this order: pg_stat_activity for that table ordered by xact_start, looking for the oldest transaction and specifically for state = idle in transaction; then pg_locks where granted = false to find the waiter; then join them on pid to name blocker and blocked. pg_blocking_pids() does that join for you and is the fastest single call.
  4. Prevent rather than merely time it better. Set lock_timeout to a second or two on the migration session so the DDL abandons the queue after a bounded wait and is retried, instead of holding it for as long as the oldest transaction lives. Be exact about what that buys: queries arriving during the wait still queue behind the pending ACCESS EXCLUSIVE request, so each attempt costs them up to one lock_timeout of added latency. The outage goes from 40 seconds to about one second per attempt, not to zero. Also run migrations away from deploy-time peaks, and put a statement timeout and an idle-in-transaction timeout on the analytics role that opens the long transactions.

Follow-up

  • The same release also wants NOT NULL on that column. What is the sequence that gets there without a long lock?
  • Your lock_timeout retry fails ten times in a row because the analytics transaction is always open. What do you change?

Suggested practice checklist

Built from the rounds and topics Castlight candidates report.

Day 1 — Map the Castlight loop

  • Write out the reported sequence: Recruiter Pre-Screen, Technical Screening, Onsite/Panel Interview, Hiring Manager Round.
  • 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 DSA (Data Structures and Algorithms)

  • Spend the session on DSA (Data Structures and Algorithms), which Castlight candidates report being tested on.
  • Write one worked example in DSA (Data Structures and Algorithms) and time yourself on it.

Deliverable: One timed worked example in DSA (Data Structures and Algorithms).

Day 3 — Work Coding interviews (whiteboard/live coding)

  • Spend the session on Coding interviews (whiteboard/live coding), which Castlight candidates report being tested on.
  • Write one worked example in Coding interviews (whiteboard/live coding) and time yourself on it.

Deliverable: One timed worked example in Coding interviews (whiteboard/live coding).

Day 4 — Work System Design

  • Spend the session on System Design, which Castlight 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: Data Structures, Algorithms & Live Coding

  • Answer aloud, timed: Implement an efficient algorithm to solve a multi-dimensional grid or matrix challenge, such as designing the core mechanics for a Minesweeper game.
  • Answer aloud, timed: Solve array, string, and graph traversal problems, explaining time and space complexity at every step.

Deliverable: Spoken answers to 2 reported Data Structures, Algorithms & Live Coding question(s), under time.

Day 6 — Answer out loud: System Design & API Architecture

  • Answer aloud, timed: Design a healthcare benefit navigation microservice capable of serving high-concurrency requests while maintaining low latency.
  • Answer aloud, timed: Walk through the design and contract definition of modern REST APIs, specifying request/response payloads, status codes, and error formats.

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

Day 7 — Answer out loud: Object-Oriented Design & Technical Fundamentals

  • Answer aloud, timed: Explain fundamental Java object-oriented concepts and demonstrate how to apply design patterns such as Singleton, Observer, Factory, and State.
  • Answer aloud, timed: Discuss backend development best practices using Spring and Spring Boot, including dependency injection and transactional management.

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

Behavioural preparation

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

How do you handle constructive criticism or conflicting technical opinions during code reviews or architectura

Medium · Behavioral & Work Style Scenarios

How do you handle constructive criticism or conflicting technical opinions during code reviews or architectural discussions?

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?

Give an example of how you prioritized technical debt against tight product delivery deadlines in a previous e

Medium · Behavioral & Work Style Scenarios

Give an example of how you prioritized technical debt against tight product delivery deadlines in a previous engineering role.

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 time when you identified a critical bug or performance bottleneck in production and how you resolve

Medium · Behavioral & Work Style Scenarios

Describe a time when you identified a critical bug or performance bottleneck in production and how you resolved it.

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?