LiveRamp Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at LiveRamp, you are at the center of a massive data ecosystem that powers identity resolution and connectivity across the advertising and marketing technology industry. Your work involves building and maintaining systems that process petabytes of data daily, ensuring that marketers can maintain seamless, privacy-conscious relationships with their customers. You aren't just writing code; you are architecting solutions for scale, distributed computing, and high-performance data pipelines. This role is critical because LiveRamp operates in a space where precision and efficiency are paramount. Whether you are working on core identity infrastructure, API tooling, or large-scale data processing engines, your code directly impacts how the world’s largest brands understand their audiences.

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

Focus areas

• HashMap / Hash Tables
• Algorithmic Problem Solving
• Graph Algorithms

The LiveRamp interview process

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

Online Assessment

Candidates begin with an online assessment to evaluate their technical skills.

What to demonstrate

• Candidates begin with an online assessment to evaluate their technical skills
• Depth in HashMap / Hash Tables

How to prepare

• Answer aloud and timed: Describe an algorithm to find the shortest path between two nodes in a massive social network.
• Answer aloud and timed: How would you implement a function to determine if two people are mutual friends in a graph?

Technical Phone Screens

One or more technical phone screens focusing on coding challenges and problem-solving.

What to demonstrate

• One or more technical phone screens focusing on coding challenges and problem-solving
• Depth in HashMap / Hash Tables

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.

Multi-Round Virtual Onsite

Candidates participate in multiple rounds of virtual onsite interviews, including system design and project discussions.

What to demonstrate

• Candidates participate in multiple rounds of virtual onsite interviews
• Including system design and project discussions

How to prepare

• Answer aloud and timed: How do you optimize memory consumption when working with very large graph datasets?
• Answer aloud and timed: How would you design a system to store billions of user records and retrieve specific IDs within a 24-hour window?

Where candidates lose points

Test your own cases

When taking the online assessment, always construct your own test cases. Many candidates who pass the basic tests still fail because they didn't account for edge cases.

Explain as you go

During phone screens, never code in silence. Narrate your logic so the interviewer can follow your thought process.

Know your resume

Be prepared to go into extreme detail on any project listed on your resume. You may be asked to redesign or optimize your own past work.

Be ready for "Why LiveRamp?

Have a thoughtful answer regarding the company’s role in the data ecosystem and why that work interests you.

Questions & practice

Describe an algorithm to find the shortest path between two nodes in a massive social network.

Medium · Graph Algorithms and Data Structures

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 would you implement a function to determine if two people are mutual friends in a graph?

Medium · Graph Algorithms and Data Structures

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?

Given a network of users, how would you store them and check if they are related?

Medium · Graph Algorithms and Data Structures

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 optimize memory consumption when working with very large graph datasets?

Medium · Graph Algorithms and Data Structures

Approach

1. Say what the runtime actually does before reasoning about the code.
2. Name what is shared across threads and what owns each piece of state.
3. Identify the window where an invariant is briefly untrue.
4. Distinguish a value from a reference to it, and say which one you handed out.

Follow-up

• What happens if two callers reach this at the same time?
• Where could this allocate more than you expect?

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?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

• A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
• The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

Explain the trade-offs between using BFS and DFS when traversing a large, distributed graph.

Medium · Graph Algorithms and Data Structures

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 system to store billions of user records and retrieve specific IDs within a 24-hour win

Medium · System Design and Scaling

How would you design a system to store billions of user records and retrieve specific IDs within a 24-hour window?

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?

If our data volume increased by 100x, how would you parallelize your current approach across multiple machines

Medium · System Design and Scaling

If our data volume increased by 100x, how would you parallelize your current approach across multiple machines?

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?

Discuss the trade-offs between different database storage strategies for high-frequency writes.

Medium · System Design and Scaling

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?

One log partition stops advancing while the others drain

Easy · poison message · head-of-line blocking · consumer lag

Search results for a subset of tenants are hours stale; the rest are current. The projection consumer reports lag of zero on 15 of 16 partitions and 400,000 on one. Its error rate is flat and its CPU is idle. outbox_event has no pending rows older than a second, so the relay has published everything it holds. Identify the mechanism, give the ordered checks, and state what you do in the first ten minutes versus what you change permanently.

Approach

1. Read the lag distribution first. A slow consumer lags everywhere; zero on fifteen partitions and 400,000 on one is not throughput. Idle CPU on the stuck partition means the consumer is not advancing its offset at all, which points at one message it cannot get past rather than at a rate problem.
2. Exonerate the producer before touching the consumer. No pending outbox rows older than a second means the relay published, so the event exists in the log. This separates never sent from sent and never applied, which are different code paths and usually different owners.
3. Read the message at the stuck offset and the handler's log lines for its event_id. A flat error rate with no progress has two explanations and you must distinguish them: the handler is throwing and the retry loop is swallowing it, or the handler is blocking on something and never returning. Idle CPU with no error lines favours the second.
4. Mitigate before diagnosing further. Move the offending event to a dead-letter store and commit the offset past it. Adding consumers does nothing here, because a partition is consumed by exactly one member of the group, and the blast radius is every aggregate hashed to that partition, not only the aggregate that produced the bad event.

Follow-up

• The dead-lettered event carried aggregate_version 7 and the projection had applied 6. What must the replay do differently if 8 and 9 landed in the meantime?
• How do you show staleness to the user while the partition is behind, given the API already returns the projection's watermark?

Suggested practice checklist

Built from the rounds and topics LiveRamp candidates report.

Day 1 — Map the LiveRamp loop

• Write out the reported sequence: Online Assessment, Technical Phone Screens, Multi-Round Virtual Onsite.
• 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 HashMap / Hash Tables

• Spend the session on HashMap / Hash Tables, which LiveRamp candidates report being tested on.
• Write one worked example in HashMap / Hash Tables and time yourself on it.

Deliverable: One timed worked example in HashMap / Hash Tables.

Day 3 — Work Algorithmic Problem Solving

• Spend the session on Algorithmic Problem Solving, which LiveRamp candidates report being tested on.
• Write one worked example in Algorithmic Problem Solving and time yourself on it.

Deliverable: One timed worked example in Algorithmic Problem Solving.

Day 4 — Work Graph Algorithms

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

Deliverable: One timed worked example in Graph Algorithms.

Day 5 — Answer out loud: Graph Algorithms and Data Structures

• Answer aloud, timed: Describe an algorithm to find the shortest path between two nodes in a massive social network.
• Answer aloud, timed: How would you implement a function to determine if two people are mutual friends in a graph?

Deliverable: Spoken answers to 2 reported Graph Algorithms and Data Structures question(s), under time.

Day 6 — Answer out loud: System Design and Scaling

• Answer aloud, timed: How would you design a system to store billions of user records and retrieve specific IDs within a 24-hour window?
• Answer aloud, timed: If our data volume increased by 100x, how would you parallelize your current approach across multiple machines?

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

Day 7 — Answer out loud: Behavioral and Project Experience

• Answer aloud, timed: Describe a project where you faced a strict deadline; how did you manage the trade-offs?
• Answer aloud, timed: Why are you interested in working at LiveRamp specifically, rather than other technology companies?

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

Behavioural preparation

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

How do you handle machine failure in a distributed data processing environment?

Medium · System Design and Scaling

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 project where you faced a strict deadline; how did you manage the trade-offs?

Medium · Behavioral and Project Experience

Approach

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

Follow-up

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

Why are you interested in working at LiveRamp specifically, rather than other technology companies?

Medium · Behavioral and Project Experience

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?

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

Medium · Behavioral and Project Experience

Approach

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

Follow-up

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

How do you handle working with legacy codebases while trying to implement new features?

Medium · Behavioral and Project Experience

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?