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SAP Concur Software Engineer Interview Guide

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

The 60-second brief

As a Software Engineer at SAP Concur, you are at the heart of the world’s leading provider of integrated travel, expense, and invoice management solutions. Your work directly impacts how millions of users manage their professional expenses, requiring you to build highly scalable, secure, and reliable systems that handle complex global financial data. You will be responsible for designing, developing, and maintaining high-performance services that integrate seamlessly into the broader SAP ecosystem. This role is not just about writing code; it is about solving complex architectural challenges in a distributed environment. You will collaborate with cross-functional teams to deliver features that improve user experience, optimize backend processes, and ensure the platform remains robust under heavy load.

This guide is scoped to a Software Engineer candidate at SAP Concur.

Focus areas

  • Virtual Interviewing (Remote Interview Process)
  • Software Engineering
  • Communication Skills (Interview Communication)

The SAP Concur interview process

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

Technical Assessments

Candidates undergo a series of technical assessments to evaluate their skills.

What to demonstrate

  • Candidates undergo a series of technical assessments to evaluate their skills
  • Depth in Virtual Interviewing (Remote Interview Process)

How to prepare

  • Answer aloud and timed: Explain the time and space complexity of your proposed solution.
  • Answer aloud and timed: How would you handle a scenario where a service in your distributed system experiences high latency?

Coding Challenges

Participants complete coding challenges to demonstrate their programming abilities.

What to demonstrate

  • Participants complete coding challenges to demonstrate their programming abilities
  • Depth in Virtual Interviewing (Remote Interview Process)

How to prepare

  • Answer aloud and timed: Describe a challenging bug you encountered and the systematic approach you took to resolve it.
  • Answer aloud and timed: How do you ensure your code is unit-testable and maintainable for long-term production use?

Architectural Discussions

In-depth discussions on system design and architecture to assess technical knowledge.

What to demonstrate

  • In-depth discussions on system design and architecture to assess technical knowledge
  • Depth in Virtual Interviewing (Remote Interview Process)

How to prepare

  • Answer aloud and timed: Compare and contrast different database indexing strategies for high-read vs. high-write workloads.
  • Answer aloud and timed: Design a system for processing expense reports that can handle thousands of concurrent requests.

Behavioral Rounds

Interviews focusing on past experiences and alignment with company values.

What to demonstrate

  • Interviews focusing on past experiences and alignment with company values
  • Depth in Virtual Interviewing (Remote Interview Process)

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 behavioral rounds above and write down what you would ask to confirm before it.

Where candidates lose points

Think out loud

During coding interviews, explain your thought process clearly. Interviewers are often more interested in how you approach a problem than in getting the perfect syntax immediately.

Master the trade-offs

Whenever you propose a solution, be ready to explain why it is better than the alternatives and what the potential downsides are.

Align with values

Research the core values of SAP and SAP Concur. Showing that you operate with the same principles will help you stand out.

Ask meaningful questions

Use the final minutes of your interviews to ask about the team's current challenges or the product roadmap. This demonstrates engagement and strategic thinking.

Questions & practice

Explain the time and space complexity of your proposed solution.

Medium · Technical Proficiency and Problem Solvin

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 challenging bug you encountered and the systematic approach you took to resolve it.

Medium · Technical Proficiency and Problem Solvin

Approach

  1. Restate the input: its shape, its size, and what is guaranteed about it.
  2. Name the brute-force solution and its complexity before improving on it.
  3. Choose the data structure from the access pattern, not from familiarity.
  4. State the target complexity and say which constraint rules the naive version out.

Follow-up

  • How does this change if the input no longer fits in memory?
  • What is the worst case, and how likely is it on real data?

How do you ensure your code is unit-testable and maintainable for long-term production use?

Medium · Technical Proficiency and Problem Solvin

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?

Compare and contrast different database indexing strategies for high-read vs. high-write workloads.

Medium · Technical Proficiency and Problem Solvin

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?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

  1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
  2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
  3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
  4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

  • A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
  • How do you honour an erasure request while resource_revision.actor_user_id still references this table?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

  1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
  2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
  3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
  4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

  • A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
  • Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

How would you handle a scenario where a service in your distributed system experiences high latency?

Medium · Technical Proficiency and Problem Solvin

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 system for processing expense reports that can handle thousands of concurrent requests.

Medium · System Design and Architecture

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 implement secure authentication and authorization across microservices?

Medium · System Design and Architecture

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 trade-offs between using a relational database versus a NoSQL store for travel booking data.

Medium · System Design and Architecture

Approach

  1. Fix the scope first: who calls this, how often, and what they do when it fails.
  2. Name the read and write paths separately; they rarely have the same bottleneck.
  3. Choose a partition key and say what query it makes expensive.
  4. State the consistency you need, and where you are willing to be stale.

Follow-up

  • What breaks first when traffic grows ten times?
  • How does this behave when that dependency is down for an hour?

How would you design a caching layer to reduce the load on your primary data store?

Medium · System Design and Architecture

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?

What strategies would you use to ensure data consistency in a distributed system?

Medium · System Design and Architecture

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 SAP Concur candidates report.

Day 1 — Map the SAP Concur loop

  • Write out the reported sequence: Technical Assessments, Coding Challenges, Architectural Discussions, Behavioral Rounds.
  • 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 Virtual Interviewing (Remote Interview Process)

  • Spend the session on Virtual Interviewing (Remote Interview Process), which SAP Concur candidates report being tested on.
  • Write one worked example in Virtual Interviewing (Remote Interview Process) and time yourself on it.

Deliverable: One timed worked example in Virtual Interviewing (Remote Interview Process).

Day 3 — Work Software Engineering

  • Spend the session on Software Engineering, which SAP Concur candidates report being tested on.
  • Write one worked example in Software Engineering and time yourself on it.

Deliverable: One timed worked example in Software Engineering.

Day 4 — Work Communication Skills (Interview Communication)

  • Spend the session on Communication Skills (Interview Communication), which SAP Concur candidates report being tested on.
  • Write one worked example in Communication Skills (Interview Communication) and time yourself on it.

Deliverable: One timed worked example in Communication Skills (Interview Communication).

Day 5 — Answer out loud: Technical Proficiency and Problem Solving

  • Answer aloud, timed: Explain the time and space complexity of your proposed solution.
  • Answer aloud, timed: How would you handle a scenario where a service in your distributed system experiences high latency?

Deliverable: Spoken answers to 2 reported Technical Proficiency and Problem Solving question(s), under time.

Day 6 — Answer out loud: System Design and Architecture

  • Answer aloud, timed: Design a system for processing expense reports that can handle thousands of concurrent requests.
  • Answer aloud, timed: How do you implement secure authentication and authorization across microservices?

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

Day 7 — Answer out loud: Behavioral and Leadership

  • Answer aloud, timed: Tell me about a time you had a technical disagreement with a peer; how did you resolve it?
  • Answer aloud, timed: Describe a situation where you had to balance the need for rapid feature delivery with technical debt.

Deliverable: Spoken answers to 2 reported Behavioral and Leadership question(s), under time.

Behavioural preparation

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

Tell me about a time you had a technical disagreement with a peer; how did you resolve it?

Medium · Behavioral and Leadership

Approach

  1. Pick a story where you made the decision, not one where you watched it.
  2. State the situation in two sentences and spend the rest on the reasoning.
  3. Give the blast radius: what could have broken, and what you measured.
  4. Name the disagreement and how you resolved it with evidence.

Follow-up

  • What would you do differently if you ran that again?
  • How did you know your change caused the improvement?

Describe a situation where you had to balance the need for rapid feature delivery with technical debt.

Medium · Behavioral and Leadership

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 mentor junior developers or contribute to a positive team culture?

Medium · Behavioral and Leadership

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 a project where you took ownership and delivered results under tight deadlines.

Medium · Behavioral and Leadership

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?