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Equativ Software Engineer Interview Guide

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

The 60-second brief

At Equativ, a Software Engineer plays a pivotal role in building, scaling, and maintaining one of the world's leading independent adtech platforms. The engineering team is responsible for unifying server-side capabilities, supply-side programmatic monetization, and demand-side tools into a seamless, high-performance ecosystem. Because Equativ handles billions of programmatic ad transactions daily, your work directly impacts the efficiency, latency, and reliability of real-time bidding (RTB) engines and ad-serving servers globally. The problems you will solve as a Software Engineer are highly complex and operate at a massive scale. You will be tasked with optimizing ultra-low latency systems where milliseconds directly translate to business revenue.

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

Focus areas

  • SQL
  • Technical Interviewing
  • Bid Request Processing

The Equativ interview process

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

Initial Conversation

Discuss your background, career aspirations, and alignment with the team's needs with a talent acquisition partner.

What to demonstrate

  • Discuss your background, career aspirations, and alignment with the team's needs with a talent acquisition partner
  • Depth in SQL

How to prepare

  • Answer aloud and timed: How does garbage collection work in.NET, and how can you optimize memory allocation in a high-throughput application?
  • Answer aloud and timed: Implement a basic algorithm to parse and filter a stream of incoming data efficiently.

Technical Evaluation

Assess practical coding and analytical skills through online assessments, take-home exercises, and live technical interviews.

What to demonstrate

  • Assess practical coding and analytical skills through online assessments, take-home exercises, and live technical interviews
  • Depth in SQL

How to prepare

  • Answer aloud and timed: Explain the difference between asynchronous and synchronous programming, and when you would choose one over the other in a web service.
  • Answer aloud and timed: How do you handle thread safety and concurrency when managing shared state in a multi-threaded backend?

Take-Home Test

Complete a comprehensive take-home test simulating real-world AdTech data discrepancies, which influences the technical onsite stage.

What to demonstrate

  • Complete a comprehensive take-home test simulating real-world AdTech data discrepancies, which influences the technical onsite stage
  • Depth in SQL

How to prepare

  • Answer aloud and timed: Write a SQL query to identify and extract specific performance metrics from a large database of ad campaigns.
  • Answer aloud and timed: How would you optimize a slow-running SQL join query that operates on tables with millions of rows?

Where candidates lose points

Master SQL basics and optimization

Do not treat database questions as an afterthought. Be ready to write clean, optimized queries on the fly and explain how indexes and execution plans work.

Understand programmatic advertising fundamentals

Before your interviews, familiarize yourself with how real-time bidding works, what an SSP and DSP do, and the basic structure of an OpenRTB bid request. This domain knowledge will help you contextualize the technical scenarios presented to you.

Be meticulous in your take-home test

Treat your take-home submission as production-grade code. Write unit tests, structure your code cleanly, document your assumptions, and explain your analytical approach to solving any data discrepancies.

Questions & practice

How does garbage collection work in.NET, and how can you optimize memory allocation in a high-throughput appli

Medium · Backend Development & Algorithms

How does garbage collection work in.NET, and how can you optimize memory allocation in a high-throughput application?

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?

Implement a basic algorithm to parse and filter a stream of incoming data efficiently.

Medium · Backend Development & 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 difference between asynchronous and synchronous programming, and when you would choose one over th

Medium · Backend Development & Algorithms

Explain the difference between asynchronous and synchronous programming, and when you would choose one over the other in a web service.

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?

How do you handle thread safety and concurrency when managing shared state in a multi-threaded backend?

Medium · Backend Development & Algorithms

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 the difference between clustered and non-clustered indexes, and how they impact write versus read perf

Medium · Database & SQL Optimization

Explain the difference between clustered and non-clustered indexes, and how they impact write versus read performance.

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?

Write a SQL query to identify and extract specific performance metrics from a large database of ad campaigns.

Medium · Database & SQL Optimization

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?

How would you optimize a slow-running SQL join query that operates on tables with millions of rows?

Medium · Database & SQL Optimization

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?

What are the key components of an OpenRTB bid request, and how do you validate incoming payload structures?

Medium · AdTech Domain & Data Analysis

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?

Describe the lifecycle of a programmatic ad auction from the moment a user loads a webpage to the rendering of

Medium · AdTech Domain & Data Analysis

Describe the lifecycle of a programmatic ad auction from the moment a user loads a webpage to the rendering of the creative.

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 do you design an ingestion pipeline to process and aggregate millions of transactional log events with min

Medium · AdTech Domain & Data Analysis

How do you design an ingestion pipeline to process and aggregate millions of transactional log events with minimal delay?

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 approach identifying the root cause of an excel discrepancy where tracked impressions do not mat

Medium · AdTech Domain & Data Analysis

How would you approach identifying the root cause of an excel discrepancy where tracked impressions do not match between an SSP and a DSP?

Approach

  1. Establish what changed and when, before forming any theory.
  2. Pick a bisection that eliminates candidates whichever way it turns out.
  3. Check the instrumentation before believing the symptom.
  4. 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 Equativ candidates report.

Day 1 — Map the Equativ loop

  • Write out the reported sequence: Initial Conversation, Technical Evaluation, Take-Home Test.
  • 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 SQL

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

Deliverable: One timed worked example in SQL.

Day 3 — Work Technical Interviewing

  • Spend the session on Technical Interviewing, which Equativ candidates report being tested on.
  • Write one worked example in Technical Interviewing and time yourself on it.

Deliverable: One timed worked example in Technical Interviewing.

Day 4 — Work Bid Request Processing

  • Spend the session on Bid Request Processing, which Equativ candidates report being tested on.
  • Write one worked example in Bid Request Processing and time yourself on it.

Deliverable: One timed worked example in Bid Request Processing.

Day 5 — Answer out loud: Backend Development & Algorithms

  • Answer aloud, timed: How does garbage collection work in.NET, and how can you optimize memory allocation in a high-throughput application?
  • Answer aloud, timed: Implement a basic algorithm to parse and filter a stream of incoming data efficiently.

Deliverable: Spoken answers to 2 reported Backend Development & Algorithms question(s), under time.

Day 6 — Answer out loud: Database & SQL Optimization

  • Answer aloud, timed: Write a SQL query to identify and extract specific performance metrics from a large database of ad campaigns.
  • Answer aloud, timed: How would you optimize a slow-running SQL join query that operates on tables with millions of rows?

Deliverable: Spoken answers to 2 reported Database & SQL Optimization question(s), under time.

Day 7 — Answer out loud: AdTech Domain & Data Analysis

  • Answer aloud, timed: What are the key components of an OpenRTB bid request, and how do you validate incoming payload structures?
  • Answer aloud, timed: How would you approach identifying the root cause of an excel discrepancy where tracked impressions do not match between an SSP and a DSP?

Deliverable: Spoken answers to 2 reported AdTech Domain & Data Analysis question(s), under time.

Behavioural preparation

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

How do you handle database locking and prevent deadlocks in high-concurrency environments?

Medium · Database & SQL Optimization

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?

Unblock an engineer without taking the keyboard

Easy · mentoring · leases · at-least-once

A teammate has spent two days on a job handler that occasionally writes duplicate rows. They are certain the queue is delivering twice by mistake. You suspect a lease expiring under a slow handler, so the job is running concurrently with itself. Describe how you have unblocked someone in this position: what you asked before offering a hypothesis, what you showed them rather than told them, and what you left them owning. Then say what you would do if their theory turned out to be the right one.

Approach

  1. Ask before diagnosing, and ask for things answerable from data they already have: the attempt count on the job rows that produced duplicates, the handler's observed duration against its lease expiry, and whether the duplicate rows share a natural key that a unique constraint could have caught.
  2. Teach the shape rather than the answer. A lease cannot distinguish a dead worker from a slow one, so a handler that outruns its lease is running twice by design, and deploys deliver the other half by killing handlers mid-run on every rollout. Both of their candidate theories produce identical duplicate rows, which is why the evidence has to come from timings rather than from argument.
  3. Hand over a checklist they execute: a natural key on every write the handler performs so the second copy collides rather than appends, the record of intent written before any external effect, a lease heartbeat while running, and the metric that shows it working.
  4. Keep ownership with them deliberately. Pair on the first write, then step back; if you finish it yourself you have closed one ticket and left the same person stuck on the next redelivery.

Follow-up

  • How would you distinguish a genuine double-delivery from a lease expiry using only the data already stored?
  • Their handler calls an external endpoint before recording that it did. What do you tell them to change first?

Tell callers you do not own that their integration breaks

Medium · deprecation · compatibility · stakeholders

A field in a write endpoint's response must change shape. You own the endpoint; you do not own the four internal callers or the outbound webhook consumers who read it. Describe a deprecation you were responsible for: what you shipped first, how you established who was actually reading the field, the window you gave and what set its length, what you did about the consumer who never moved, and how you decided removal was safe. Name the signal you used, not the announcement you sent.

Approach

  1. Establish the reader set empirically rather than from a wiki of owners: per-field usage counters keyed by principal, or access logs attributed to a consumer. State the blind spot of whichever you pick, since a consumer that reads the field only on a monthly job will not appear in a week of logs.
  2. Ship additive first. Populate the new field alongside the old one so no reader is forced to move, which is also what keeps a rolling deploy safe, because old and new instances answer the same requests at the same time and a rollback must still find the old shape present.
  3. Set the window from the slowest legitimate consumer's release cadence, not from your calendar, and decide separately what to do for a consumer with no release process at all, such as an external webhook endpoint you can only email.
  4. Convert silence into evidence before you rely on it: a short, low-traffic removal window that makes a still-dependent consumer fail visibly and loudly while you are watching, rather than at three in the morning after you have moved on.

Follow-up

  • How would you detect a consumer that reads the field only during a monthly export?
  • One caller refuses to move and has a commercial relationship behind it. What changes in your plan and what does not?