Interview guides · Glide

Glide Software Engineer Interview Guide

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

The 60-second brief

At Glide, a Software Engineer is more than just a coder; you are a builder of the infrastructure that bridges the gap between legacy financial systems and the modern digital world. Our mission is to empower community banks and credit unions to offer sophisticated, novel digital experiences, and your work directly influences the financial accessibility and stability of our end users. Whether you are working on our core fintech platform or managing critical Salesforce workflow automations, you are tasked with solving complex problems that require high levels of security, scalability, and performance. You will operate in a fast-moving, collaborative environment where your code has immediate, measurable impact. This role is for engineers who thrive when given the autonomy to own a feature from initial design and architecture to deployment and long-term maintenance.

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

Focus areas

Workflow Automation (Salesforce)
Salesforce Platform (Ecosystem Administration)
React

The Glide interview process

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

Initial Screen

The first step involves an initial screening to assess candidate fit.

What to demonstrate

The first step involves an initial screening to assess candidate fit
Depth in Workflow Automation (Salesforce)

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.

Deep-Dive Technical Discussions

Candidates participate in a series of in-depth technical discussions.

What to demonstrate

Candidates participate in a series of in-depth technical discussions
Depth in Workflow Automation (Salesforce)

How to prepare

Answer aloud and timed: Can you explain the trade-offs between different data fetching libraries like TanStack Query and tRPC?
Answer aloud and timed: How do you ensure security when building Node.js APIs that handle sensitive financial data?

Final Round

The final round focuses on team fit and product vision.

What to demonstrate

The final round focuses on team fit and product vision
Depth in Workflow Automation (Salesforce)

How to prepare

Answer aloud and timed: Walk me through your process for debugging a performance bottleneck in a NextJS application.
Answer aloud and timed: What strategies do you use for writing effective test cases for Apex or JavaScript components?

Where candidates lose points

Own your narrative

Be prepared to talk about your past projects with authority. You should know every trade-off you made and why you made it.

Ask meaningful questions

Use your time with interviewers to learn about our product roadmap and technical challenges. It shows you are already thinking like a member of the team.

Focus on the "Why

When explaining a technical decision, always explain the context. Why was this the best choice for this specific problem at this specific time?

Be ready for whiteboard sessions

Whether remote or in-person, be prepared to draw out your architectural ideas clearly.

Questions & practice

Diff a projection against the primary without per-row point reads

Hard · reconciliation · range hashing · throttling

The listing projection has drifted and some rows show a stale version. The primary holds 40,000,000 resource rows across 12,000 tenants while serving 1,200 writes and 14,000 reads per second. The obvious repair, reading each resource row and comparing its version against the projection, is correct and would eventually finish. Explain precisely why it is unacceptable here, then give a diff that finds the differing rows, state its complexity, and make it safe to run against a live primary. Replication lag is usually under 100 ms and is not bounded.

Approach

Quantify the naive cost rather than calling it slow: 40,000,000 point reads at even 0.5 ms each is over five hours serialised, and the only lever is concurrency, which is exactly what you cannot spend. The primary's pool is sized for the write path, and 40,000,000 random reads evict the buffer cache that sustains the 85 percent cache hit rate, so the audit degrades the system it is auditing.
Replace random access with one ordered pass per side. Both sides can be read in (tenant_id, resource_id) order, which is a sequential scan on each and a merge join in O(n) time and O(1) memory. For a dense diff that is the whole answer, and it reads the primary once instead of 40,000,000 times.
For the expected sparse case, compare range hashes instead of rows: partition the key space, compute per range an order-independent aggregate over hash(resource_id, version), compare aggregates, and descend only into ranges that differ. With d differing rows and branching factor B, at most d ranges mismatch per level, so the drill-down examines O(d log_B(n/d)) ranges and reads full rows only in mismatching leaves.
Aggregate with a sum modulo 2^64 or a multiset hash, never XOR. XOR is order-independent but self-cancelling, so two rows wrong in the same way, or a row duplicated on one side, leave the range aggregate matching and the range is declared clean.

Follow-up

The diff reports 900 stale rows. How do you decide between patching those rows and rebuilding the projection from resource_revision?
Same job, but the projection lives in a search index that cannot be scanned in key order. What changes?

Merge partitioned event streams into one ordered feed with bounded lateness

Hard · k-way merge · watermarks · out-of-order streams

The read-model service consumes 64 log partitions carrying about 4,000 events per second in total. Each partition is ordered within itself, but partitions drift by up to 30 seconds, and the activity feed must present a tenant's events in occurred_at order. Produce the merge. State its complexity, the buffer it requires in events and in bytes, what happens when one partition is idle, and what you do with an event that arrives after you have already emitted its position. Payloads average 1 KB.

Approach

Merge with a min-heap over the 64 partition heads keyed on (occurred_at, event_id): O(log P) per event and O(n log P) overall. The tie-break on event_id is what makes the output deterministic when two partitions carry the same millisecond, which matters because the feed is paginated and a non-deterministic order reorders pages under the reader.
Emitting the heap head is only correct once every partition has produced everything up to that timestamp, so the emit condition is a watermark: the minimum across partitions of the highest occurred_at seen, less the allowed lateness. Events are held until the watermark passes them, which is what turns individually ordered streams into a jointly ordered one.
Size the buffer from the lateness rather than guessing: 4,000 events per second times 30 seconds is 120,000 buffered events, and at 1 KB each about 120 MB of heap. That number is the real price of the ordering guarantee and belongs in front of whoever asked for it.
Handle the idle partition explicitly, because it fails the feed rather than corrupting it: a partition with no traffic never advances its own maximum, so the watermark freezes and output stops entirely. Either every partition emits a periodic idle marker carrying the broker's current time, or the watermark falls back to wall clock for a partition silent beyond a threshold.

Follow-up

The lateness budget is raised to five minutes. What is the new buffer, and what besides memory changes?
The consumer restarts. Where does it resume from, and what does the feed look like for the first 30 seconds?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Can you explain the trade-offs between different data fetching libraries like TanStack Query and tRPC?

Medium · Technical Proficiency & Full Stack Devel

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?

Replace offset paging on the resource feed with keyset

Easy · keyset pagination · composite index · sort order

resource holds resource_id, tenant_id, owner_user_id, title, body_ref, version, status ('draft','active','archived','deleted'), created_at, updated_at, deleted_at, with an index on (tenant_id, status, updated_at DESC, resource_id DESC). The listing endpoint returns active resources for one tenant, newest update first, 50 per page, today with LIMIT 50 OFFSET n. Tenants reach page 400 and rows are created while they read. Write the keyset query, define what the cursor carries and how it is encoded, and say which part of the index each predicate uses. Assume PostgreSQL 16.

Approach

Name the two failures separately. OFFSET 20000 makes the server produce and discard 20,000 rows, so page cost grows with depth rather than with page size. Independently, any write that changes how many rows sort above the offset moves the window between two fetches, and the direction decides which anomaly you get: an insert lands at the head of updated_at DESC and pushes already-returned rows down past the boundary, so they are returned a second time; a delete above the offset, or a row whose updated_at is bumped above the cursor, pulls rows up and one is never returned at all. Nothing in the response reveals either.
Write the seek: WHERE tenant_id = $1 AND status = 'active' AND (updated_at, resource_id) < ($2, $3) ORDER BY updated_at DESC, resource_id DESC LIMIT 50. The row-value comparison is one index range rather than a disjunction, and both columns are NOT NULL, which is what makes that comparison well defined.
Map each predicate onto the index: tenant_id and status are equality on the leading columns, (updated_at, resource_id) is the range, and the ORDER BY matches the index order so no Sort node appears and the scan stops after 50 rows. The DESC in the definition only matters for mixed directions — a plain ascending btree on the same columns is read backwards for this query.
Put both sort columns in the cursor and nothing the client can tamper with into another tenant: base64 of (updated_at, resource_id), validated server-side, with tenant_id taken from the principal.

Follow-up

The client asks for 'jump to page 400'. What do you offer instead, and what does the honest version cost?
Sort order becomes user-selectable across four columns. How many indexes is that, and which would you refuse to add?

These questions test your hands-on experience with our core technology stack and your ability to write clean,

Medium · Technical Proficiency & Full Stack Devel

These questions test your hands-on experience with our core technology stack and your ability to write clean, maintainable, and secure code.

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
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 state management in a large-scale React application?

Medium · Technical Proficiency & Full Stack Devel

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
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 ensure security when building Node.js APIs that handle sensitive financial data?

Medium · Technical Proficiency & Full Stack Devel

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

What strategies do you use for writing effective test cases for Apex or JavaScript components?

Medium · Technical Proficiency & Full Stack Devel

Approach

Clarify what is being asked and what a complete answer contains.
State your assumptions explicitly before working the problem.
Say what you would check first and why it is the highest-information step.
Work from the requirement backwards to the design.

Follow-up

What assumption would you test first?
How would you know your answer was wrong?

We look for your ability to think about long-term scalability and the reliability of the systems you build.

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?

How would you design a scalable architecture to support hundreds of thousands of users?

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?

What are the key considerations when integrating Salesforce with external systems via REST APIs?

Medium · System Design & Architecture

Approach

Say who the caller is and what they do when the call fails halfway.
Define the identity of a request so a retry cannot double-apply it.
Separate accepted, pending, failed and confirmed; they are different facts.
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 decide between a microservices approach versus a modular monolith for a new product feature?

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?

Describe your process for refactoring legacy code while maintaining system uptime.

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?

How do you approach data modeling to ensure consistency and performance across different services?

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?

Walk me through your process for debugging a performance bottleneck in a NextJS application.

Medium · Technical Proficiency & Full Stack Devel

Approach

Establish what changed and when, before forming any theory.
Pick a bisection that eliminates candidates whichever way it turns out.
Check the instrumentation before believing the symptom.
Separate the trigger from the cause; the deploy is rarely the bug.

Follow-up

What would you look at first, and what would it rule out?
How would you tell a cause from a coincidence here?

Suggested practice checklist

Built from the rounds and topics Glide candidates report.

Day 1 — Map the Glide loop

Write out the reported sequence: Initial Screen, Deep-Dive Technical Discussions, Final 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 3 reported rounds, with the weakest marked.

Day 2 — Work Workflow Automation (Salesforce)

Spend the session on Workflow Automation (Salesforce), which Glide candidates report being tested on.
Write one worked example in Workflow Automation (Salesforce) and time yourself on it.

Deliverable: One timed worked example in Workflow Automation (Salesforce).

Day 3 — Work Salesforce Platform (Ecosystem Administration)

Spend the session on Salesforce Platform (Ecosystem Administration), which Glide candidates report being tested on.
Write one worked example in Salesforce Platform (Ecosystem Administration) and time yourself on it.

Deliverable: One timed worked example in Salesforce Platform (Ecosystem Administration).

Day 4 — Work React

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

Deliverable: One timed worked example in React.

Day 5 — Answer out loud: Technical Proficiency & Full Stack Development

Answer aloud, timed: These questions test your hands-on experience with our core technology stack and your ability to write clean, maintainable, and secure code.
Answer aloud, timed: How do you approach state management in a large-scale React application?

Deliverable: Spoken answers to 2 reported Technical Proficiency & Full Stack Development question(s), under time.

Day 6 — Answer out loud: System Design & Architecture

Answer aloud, timed: We look for your ability to think about long-term scalability and the reliability of the systems you build.
Answer aloud, timed: How would you design a scalable architecture to support hundreds of thousands of users?

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

Day 7 — Answer out loud: Behavioral & Problem Solving

Answer aloud, timed: These questions evaluate how you navigate ambiguity, handle feedback, and contribute to a team.
Answer aloud, timed: Tell me about a time you had to pivot your technical approach due to shifting business requirements.

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