cswork / libraryOpen practice workspace

Interview guides · Reward Gateway

Reward Gateway Software Engineer Interview Guide

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

The 60-second brief

A Software Engineer at Reward Gateway plays a pivotal role in designing, building, and scaling the technology that powers employee engagement for millions of users worldwide. The engineering team is responsible for delivering a highly available, secure SaaS platform that integrates employee recognition, reward programs, internal communications, and surveys. Your work directly impacts how organizations build stronger cultures, meaning the code you write must be robust, scalable, and highly user-centric. The engineering organization at Reward Gateway works on complex technical challenges, including real-time transaction processing, deep integrations with global HR Information Systems (HRIS), and high-traffic web applications.

This guide is scoped to a Software Engineer candidate at Reward Gateway.

Focus areas

  • Technical Interview Preparation
  • Take-Home Assignments
  • Backend Engineering

The Reward Gateway interview process

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

Recruiter Call

Brief conversation with a member of the talent acquisition team to discuss your background and align on expectations.

What to demonstrate

  • Brief conversation with a member of the talent acquisition team to discuss your background and align on expectations
  • Depth in Technical Interview Preparation

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.

Take-Home Assignment

Complete a flexible, language-agnostic take-home assignment to showcase your engineering skills.

What to demonstrate

  • Complete a flexible, language-agnostic take-home assignment to showcase your engineering skills
  • Depth in Technical Interview Preparation

How to prepare

  • Answer aloud and timed: Describe your experience with RESTful API design. What makes an API clean and intuitive for other developers to consume?
  • Answer aloud and timed: How do you ensure your code is secure against common vulnerabilities like SQL injection or Cross-Site Scripting (XSS)?

Technical Review

Participate in a deeper technical review to discuss your take-home assignment and technical skills.

What to demonstrate

  • Participate in a deeper technical review to discuss your take-home assignment and technical skills
  • Depth in Technical Interview Preparation

How to prepare

  • Answer aloud and timed: Why did you choose this specific programming language and directory structure for the take-home task?
  • Answer aloud and timed: How would you refactor your solution if the volume of data processed by this service scaled by a factor of one hundred?

Culture Fit Conversation

Engage in a conversation with engineering leadership to assess cultural alignment.

What to demonstrate

  • Engage in a conversation with engineering leadership to assess cultural alignment
  • Depth in Technical Interview Preparation

How to prepare

  • Prepare three examples from your own work, each with a decision you made and an outcome you can quantify.
  • Re-read the description of the culture fit conversation above and write down what you would ask to confirm before it.

Where candidates lose points

Document your assumptions

When submitting your take-home task, use the README to clearly explain any assumptions you made about the requirements, as well as any trade-offs you chose to make.

Focus on the fundamentals

You do not need to memorize complex algorithms. Instead, focus on demonstrating deep knowledge of clean code, OOP or functional programming principles, database optimization, and testing.

Research the company culture

Reward Gateway is highly mission-driven. Take time to read about their products and core values, and prepare examples of how your personal working style aligns with their culture of collaboration and innovation.

Prepare thoughtful questions

The final interview with engineering leadership is a two-way street. Prepare insightful questions about their technical roadmap, engineering culture, and how they support career development.

Questions & practice

Identify the heaviest tenants in a five-minute window under memory pressure

Medium · top-k · heavy hitters · streaming

The edge service handles about 3,000 requests per second across roughly 50,000 tenants, peaking near 9,000. Expose the 50 heaviest tenants by request count over the trailing five minutes so limits can be tightened before one tenant's backfill starves the fleet. You may not retain five minutes of raw records. Give the exact solution and its memory, then the bounded-memory approximation with its error stated as a formula, and say which you would ship and at what tenant cardinality that choice changes.

Approach

  1. Do the exact version first, because it is affordable at this cardinality: a ring of 300 one-second counters per tenant, advanced lazily, is 1,200 bytes of counters per tenant and roughly 60 to 90 MB for 50,000 tenants with overhead. Carry a running total and subtract the bucket you overwrite so a window read is O(1) rather than 300 adds.
  2. Extract the top 50 with a size-k min-heap over the tenant sums: O(d log k) for d tenants, against O(d log d) to sort them all. Maintaining the heap continuously instead of on query requires a tenant-to-heap-index map, because incrementing a count already inside the heap means sifting from a known position, and without that map you rebuild the heap on every request.
  3. State the approximation precisely rather than gesturing at sketches. Misra-Gries with m counters retains every item whose true count exceeds N/(m+1), and each retained count underestimates the truth by at most N/(m+1). With m = 1,000 and N = 900,000 requests in the window the error is roughly 900 requests, which is fine for spotting a tenant sending 50,000 and useless for ranking two tenants 200 apart.
  4. Say what breaks when the window slides: Misra-Gries and Space-Saving are insert-only and cannot be decremented as records age out. The workable construction is one summary per sub-window, say ten seconds, with 30 summaries merged at query time, and the merged error is the sum of the per-summary errors, so the bound degrades linearly in the number of sub-windows.

Follow-up

  • The heaviest tenant is heavy because of one export job rather than user traffic. Should the limiter treat those as the same tenant?
  • Two tenants sit tied at the boundary of the top 50. Does your answer flap, and does the flapping matter?

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

  1. Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
  2. Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
  3. Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
  4. Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

  • The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
  • The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

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

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

How do you ensure your code is secure against common vulnerabilities like SQL injection or Cross-Site Scriptin

Medium · Technical & Architecture Questions

How do you ensure your code is secure against common vulnerabilities like SQL injection or Cross-Site Scripting (XSS)?

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?

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 relational and non-relational databases, and when you would choose one over the

Medium · Technical & Architecture Questions

Explain the difference between relational and non-relational databases, and when you would choose one over the other.

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 error handling and logging in a distributed system?

Medium · Technical & Architecture Questions

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 your experience with RESTful API design. What makes an API clean and intuitive for other developers t

Medium · Technical & Architecture Questions

Describe your experience with RESTful API design. What makes an API clean and intuitive for other developers to consume?

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?

Why did you choose this specific programming language and directory structure for the take-home task?

Medium · Take-Home Task Discussion

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 refactor your solution if the volume of data processed by this service scaled by a factor of one

Medium · Take-Home Task Discussion

How would you refactor your solution if the volume of data processed by this service scaled by a factor of one hundred?

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?

Walk us through your testing strategy for this task. Why did you choose these specific unit and integration te

Medium · Take-Home Task Discussion

Walk us through your testing strategy for this task. Why did you choose these specific unit and integration tests?

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?

If you had more time, what features or architectural improvements would you add to this codebase?

Medium · Take-Home Task Discussion

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 Reward Gateway candidates report.

Day 1 — Map the Reward Gateway loop

  • Write out the reported sequence: Recruiter Call, Take-Home Assignment, Technical Review, Culture Fit Conversation.
  • 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 Technical Interview Preparation

  • Spend the session on Technical Interview Preparation, which Reward Gateway candidates report being tested on.
  • Write one worked example in Technical Interview Preparation and time yourself on it.

Deliverable: One timed worked example in Technical Interview Preparation.

Day 3 — Work Take-Home Assignments

  • Spend the session on Take-Home Assignments, which Reward Gateway candidates report being tested on.
  • Write one worked example in Take-Home Assignments and time yourself on it.

Deliverable: One timed worked example in Take-Home Assignments.

Day 4 — Work Backend Engineering

  • Spend the session on Backend Engineering, which Reward Gateway candidates report being tested on.
  • Write one worked example in Backend Engineering and time yourself on it.

Deliverable: One timed worked example in Backend Engineering.

Day 5 — Answer out loud: Technical & Architecture Questions

  • Answer aloud, timed: Explain the difference between relational and non-relational databases, and when you would choose one over the other.
  • Answer aloud, timed: How do you approach error handling and logging in a distributed system?

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

Day 6 — Answer out loud: Take-Home Task Discussion

  • Answer aloud, timed: Why did you choose this specific programming language and directory structure for the take-home task?
  • Answer aloud, timed: How would you refactor your solution if the volume of data processed by this service scaled by a factor of one hundred?

Deliverable: Spoken answers to 2 reported Take-Home Task Discussion question(s), under time.

Day 7 — Answer out loud: Behavioral & Culture Fit

  • Answer aloud, timed: Describe a time when you had a technical disagreement with a teammate or lead developer. How did you resolve it?
  • Answer aloud, timed: How do you manage your time and prioritize tasks when working on multiple high-priority features simultaneously?

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

Behavioural preparation

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

Describe a time when you had a technical disagreement with a teammate or lead developer. How did you resolve i

Medium · Behavioral & Culture Fit

Describe a time when you had a technical disagreement with a teammate or lead developer. How did you resolve 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?

How do you manage your time and prioritize tasks when working on multiple high-priority features simultaneousl

Medium · Behavioral & Culture Fit

How do you manage your time and prioritize tasks when working on multiple high-priority features simultaneously?

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?

Talk about a project that did not go as planned. What went wrong, and what did you learn from the experience?

Medium · Behavioral & Culture 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 stay updated with the latest engineering trends and apply them to your daily work?

Medium · Behavioral & Culture 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?