Placed Software Engineer Interview Guide

The 60-second brief

As a Software Engineer at Placed, you are at the intersection of large-scale data processing and actionable consumer insights. Your work directly impacts how businesses measure the effectiveness of their advertising by bridging the gap between digital exposure and real-world store visits. You will be tasked with building robust, scalable systems that handle complex datasets, requiring both high-level architectural thinking and precise implementation. The environment at Placed is fast-paced and data-centric. Engineers here are expected to be more than just coders; you are expected to be problem solvers who understand the nuances of the product. Whether you are optimizing data pipelines or refining the core algorithms that power the company’s attribution models, your contributions will be central to the company’s ability to provide value to its clients.

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

Focus areas

• Coding Interview Problem Solving
• Anagram Grouping Logic
• Big Data / Hadoop Experience

The Placed interview process

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

Recruiter Screen

Initial screening call with a recruiter to assess your background and fit for the role.

What to demonstrate

• Initial screening call with a recruiter to assess your background and fit for the role
• Depth in Coding Interview Problem Solving

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 Phone Screen

A technical phone interview to evaluate your technical competency.

What to demonstrate

• A technical phone interview to evaluate your technical competency
• Depth in Coding Interview Problem Solving

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.

In-Person/Virtual Loop

Multi-round interviews with individual contributors and engineering managers to assess both technical skills and culture fit.

What to demonstrate

• Multi-round interviews with individual contributors and engineering managers to assess both technical skills and culture fit
• Depth in Coding Interview Problem Solving

How to prepare

• Answer aloud and timed: How have you handled data consistency in distributed systems?
• Answer aloud and timed: Can you walk through a complex technical project you led and the specific architectural decisions you made?

Where candidates lose points

Communicate your thought process

Even if you are stuck, talk through what you are thinking. The interviewer wants to see how you approach problems, not just the final output.

Be honest about your stack

If you don't have experience with a specific tool, don't bluff. Focus on your ability to pick up new tools based on your past successes.

Review your resume

Be prepared to discuss every technical detail listed on your resume in depth.

Clarify the role

Ensure you have a clear understanding of the team's focus; ask specifically if the role is backend, frontend, or data-focused to avoid mismatched expectations.

Questions & practice

Given a list of words, print out the words that are anagrams of each other.

Medium · Coding and Algorithms

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 optimize the search time for a large set of strings?

Medium · Coding and Algorithms

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?

Explain the time and space complexity of your solution for the provided coding challenge.

Medium · Coding and Algorithms

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?

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

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

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?

How have you handled data consistency in distributed systems?

Medium · Technical Domain and Experience

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?

Can you walk through a complex technical project you led and the specific architectural decisions you made?

Medium · Technical Domain and Experience

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?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

• Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
• How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

Built from the rounds and topics Placed candidates report.

Day 1 — Map the Placed loop

• Write out the reported sequence: Recruiter Screen, Technical Phone Screen, In-Person/Virtual Loop.
• 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 Coding Interview Problem Solving

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

Deliverable: One timed worked example in Coding Interview Problem Solving.

Day 3 — Work Anagram Grouping Logic

• Spend the session on Anagram Grouping Logic, which Placed candidates report being tested on.
• Write one worked example in Anagram Grouping Logic and time yourself on it.

Deliverable: One timed worked example in Anagram Grouping Logic.

Day 4 — Work Big Data / Hadoop Experience

• Spend the session on Big Data / Hadoop Experience, which Placed candidates report being tested on.
• Write one worked example in Big Data / Hadoop Experience and time yourself on it.

Deliverable: One timed worked example in Big Data / Hadoop Experience.

Day 5 — Answer out loud: Coding and Algorithms

• Answer aloud, timed: Given a list of words, print out the words that are anagrams of each other.
• Answer aloud, timed: How would you optimize the search time for a large set of strings?

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

Day 6 — Answer out loud: Technical Domain and Experience

• Answer aloud, timed: Describe your experience with large-scale data processing frameworks like Hadoop.
• Answer aloud, timed: How have you handled data consistency in distributed systems?

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

Day 7 — Answer out loud: Behavioral and Culture Fit

• Answer aloud, timed: Why are you interested in the intersection of advertising and location data?
• Answer aloud, timed: Tell me about a time you had to clarify ambiguous project requirements.

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

Behavioural preparation

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

Describe your experience with large-scale data processing frameworks like Hadoop.

Medium · Technical Domain and 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 the intersection of advertising and location data?

Medium · Behavioral and 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?

Tell me about a time you had to clarify ambiguous project requirements.

Medium · Behavioral and 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 handle feedback from non-technical stakeholders?

Medium · Behavioral and 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?