A Software Engineer at Metadata plays a pivotal role in building and scaling the industry's leading B2B marketing operating system. The platform automates manual tasks, optimizes campaigns, and drives revenue by integrating complex advertising channels, CRMs, and marketing automation tools. As an engineer here, you will be responsible for designing and implementing robust backend systems that can process massive amounts of data, orchestrate complex workflows, and integrate seamlessly with third-party APIs. The impact of this position is felt directly by our customers, who rely on the platform's speed, reliability, and precision to run their marketing campaigns. You will work on solving highly technical challenges, such as optimizing database queries for high-throughput data pipelines, containerizing services for seamless deployment, and designing highly scalable REST and Graph APIs. This role is ideal for engineers who thrive in a fast-paced environment and enjoy ownership over their code from design to production. At Metadata, you are not just writing code; you are building the foundation of an automated marketing ecosystem that demands high performance, clean architecture, and continuous innovation.
Technical Screening
reportedInitial assessment focusing on practical technical skills and coding abilities.
What to demonstrate
- Initial assessment focusing on practical technical skills and coding abilities
- Depth in Java
How to prepare
- Answer aloud and timed: Explain the N+1 select problem in Hibernate. How does it occur, and what are the primary strategies to resolve it?
- Answer aloud and timed: How would you implement a custom
Setdata structure in Java from scratch, assuming the standardjava.util.Setinterface and its implementations were completely unavailable?
Deep-Dive Coding
reportedIn-depth coding interview where candidates build functional applications.
What to demonstrate
- In-depth coding interview where candidates build functional applications
- Depth in Java
How to prepare
- Answer aloud and timed: Discuss the core differences between Java 8 Streams and traditional loops. In what scenarios would a stream be less efficient?
- Answer aloud and timed: Explain how the Spring Boot application lifecycle works, and how you would configure a basic REST API using Spring Boot, Gradle, and MySQL.
System Design Assessment
reportedEvaluation of candidates' ability to design systems and architectures.
What to demonstrate
- Evaluation of candidates' ability to design systems and architectures
- Depth in Java
How to prepare
- Answer aloud and timed: Simulate a linked list implementation in Java. Explain the time complexity (Big O notation) for searching, inserting, and deleting nodes.
- Answer aloud and timed: Write a recursive function to solve a string manipulation problem, and discuss how you would optimize it to prevent stack overflow errors.
Leadership Review
reportedFinal assessment focusing on behavioral alignment and leadership qualities.
What to demonstrate
- Final assessment focusing on behavioral alignment and leadership qualities
- Depth in Java
How to prepare
- Answer aloud and timed: How do you implement a queue using two stacks? Explain the amortized time complexity of the enqueue and dequeue operations.
- Answer aloud and timed: What is database normalization and denormalization? Provide a practical scenario where denormalizing a database schema is beneficial for performance.
PracHub editorial advice for the preparation topics above.
Prioritize Clean Code over Speed
During live coding, take a moment to structure your code cleanly. Use descriptive variable names, follow OOP principles, and write unit tests if time permits.
Master SQL Joins and Grouping
Expect to write SQL queries on the spot. Be very comfortable with LEFT JOIN, INNER JOIN, and GROUP BY operations, as these are frequently tested.
Understand Hibernate Internals
Be ready to discuss how Hibernate interacts with the database. Understanding how to prevent the N+1 select problem is highly valued.
Be Ready for Custom Implementations
You may be asked to implement standard data structures (like a Set or Linked List) from scratch. Practice these fundamental implementations before your interview.
Choose a category, try a prompt, then open its approach, worked solution or follow-up when you need it.
Simulate a linked list implementation in Java. Explain the time complexity (Big O notation) for searching, ins
Simulate a linked list implementation in Java. Explain the time complexity (Big O notation) for searching, inserting, and deleting nodes.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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 recursive function to solve a string manipulation problem, and discuss how you would optimize it to pr
Write a recursive function to solve a string manipulation problem, and discuss how you would optimize it to prevent stack overflow errors.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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 implement a queue using two stacks? Explain the amortized time complexity of the enqueue and dequeu
How do you implement a queue using two stacks? Explain the amortized time complexity of the enqueue and dequeue operations.
Approach
- Restate the input: its shape, its size, and what is guaranteed about it.
- Name the brute-force solution and its complexity before improving on it.
- Choose the data structure from the access pattern, not from familiarity.
- 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?
What is database normalization and denormalization? Provide a practical scenario where denormalizing a databas
What is database normalization and denormalization? Provide a practical scenario where denormalizing a database schema is beneficial for performance.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- 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?
Imagine a database schema with tables for `Students`, `Courses`, and `Enrollments`. Write a SQL query utilizin
Imagine a database schema with tables for Students, Courses, and Enrollments. Write a SQL query utilizing multiple JOIN operations and a GROUP BY clause to find courses with more than a specific number of students.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- 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 do you diagnose and improve slow-running SQL queries? Explain the role of indexes, execution plans, and qu
How do you diagnose and improve slow-running SQL queries? Explain the role of indexes, execution plans, and query refactoring.
Approach
- Name the grain you start from and join outward from it.
- Check whether any join is one-to-many before aggregating, or the sums inflate.
- Say which index the query would use, and what makes it unusable.
- 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 the N+1 select problem in Hibernate. How does it occur, and what are the primary strategies to resolve
Explain the N+1 select problem in Hibernate. How does it occur, and what are the primary strategies to resolve it?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- 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 implement a custom `Set` data structure in Java from scratch, assuming the standard `java.util.S
How would you implement a custom Set data structure in Java from scratch, assuming the standard java.util.Set interface and its implementations were completely unavailable?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Discuss the core differences between Java 8 Streams and traditional loops. In what scenarios would a stream be
Discuss the core differences between Java 8 Streams and traditional loops. In what scenarios would a stream be less efficient?
Approach
- Clarify what is being asked and what a complete answer contains.
- State your assumptions explicitly before working the problem.
- Say what you would check first and why it is the highest-information step.
- Work from the requirement backwards to the design.
Follow-up
- What assumption would you test first?
- How would you know your answer was wrong?
Explain how the Spring Boot application lifecycle works, and how you would configure a basic REST API using Sp
Explain how the Spring Boot application lifecycle works, and how you would configure a basic REST API using Spring Boot, Gradle, and MySQL.
Approach
- Say who the caller is and what they do when the call fails halfway.
- Define the identity of a request so a retry cannot double-apply it.
- Separate accepted, pending, failed and confirmed; they are different facts.
- 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?
Every query on one table stalls for forty seconds mid-deploy
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
- 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.
- 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.
- 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.
- 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?
Built from the rounds and topics Metadata candidates report.
Prepare, practise & reflect
One practical outcome each day. Spend longer where you need it.
0 / 7 done01Map the Metadata loop
- Write out the reported sequence: Technical Screening, Deep-Dive Coding, System Design Assessment, Leadership Review.
- 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.
02Work Java
- Spend the session on Java, which Metadata candidates report being tested on.
- Write one worked example in Java and time yourself on it.
Deliverable: One timed worked example in Java.
03Work REST API Development
- Spend the session on REST API Development, which Metadata candidates report being tested on.
- Write one worked example in REST API Development and time yourself on it.
Deliverable: One timed worked example in REST API Development.
04Work N+1 Query Problem (ORM Performance)
- Spend the session on N+1 Query Problem (ORM Performance), which Metadata candidates report being tested on.
- Write one worked example in N+1 Query Problem (ORM Performance) and time yourself on it.
Deliverable: One timed worked example in N+1 Query Problem (ORM Performance).
05Answer out loud: Java & Backend Frameworks
- Answer aloud, timed: Explain the N+1 select problem in Hibernate. How does it occur, and what are the primary strategies to resolve it?
- Answer aloud, timed: How would you implement a custom `Set` data structure in Java from scratch, assuming the standard `java.util.Set` interface and its implementations were completely unavailable?
Deliverable: Spoken answers to 2 reported Java & Backend Frameworks question(s), under time.
06Answer out loud: Data Structures & Algorithms
- Answer aloud, timed: Simulate a linked list implementation in Java. Explain the time complexity (Big O notation) for searching, inserting, and deleting nodes.
- Answer aloud, timed: Write a recursive function to solve a string manipulation problem, and discuss how you would optimize it to prevent stack overflow errors.
Deliverable: Spoken answers to 2 reported Data Structures & Algorithms question(s), under time.
07Answer out loud: Database & Query Optimization
- Answer aloud, timed: What is database normalization and denormalization? Provide a practical scenario where denormalizing a database schema is beneficial for performance.
- Answer aloud, timed: Imagine a database schema with tables for `Students`, `Courses`, and `Enrollments`. Write a SQL query utilizing multiple `JOIN` operations and a `GROUP BY` clause to find courses with more than a specific number of students.
Deliverable: Spoken answers to 2 reported Database & Query Optimization question(s), under time.
Expand any day for tasks and deliverables. Your progress is saved on this device.
Behavioural rounds judge the decision you made and what it cost.
Turn a code review disagreement into a decision
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
Approach
- Sort the disagreement before writing anything. A silently discarded write is a correctness claim about data; the choice between 409 and 412 is taste. Only the first justifies blocking a merge, and saying which one you are doing is most of the value of the comment.
- Make the claim reproducible in the comment itself with an interleaving rather than a principle: A reads version 7, B reads version 7, B commits version 8, A's predicate matches zero rows, A is told it succeeded and A's edit is gone.
- Offer the alternative with its cost attached: return 409 carrying the current version and the revision that won, so the client can re-read and re-apply. Note that automatic retry is not the fix, because a retry re-reads the winner's state and reapplies an intent formed against data that no longer exists.
- Apply an escalation rule you can state: two round trips on the thread, then a call, and the service's owner decides rather than the reviewer. A reviewer who cannot be overruled is a bottleneck with extra steps.
Follow-up
- Where would you put the test that fails if someone reintroduces the swallowed zero rowcount?
- The author says clients cannot handle a 409. How do you check whether that is true?
Argue against a design, lose, and commit anyway
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
Approach
- State the prediction in falsifiable form up front: the mechanism, the condition that triggers it, and the observable outcome. A prediction that cannot be checked also cannot be credited to you later.
- Show the evidence you had at the time and label each piece honestly as measured, analogous, or intuition. Keeping the intuition is fine; disguising it as data is the thing that erodes your standing in the next argument.
- Represent the opposing case at full strength, including the constraint you did not control: a fixed date, a team boundary, or the fact that the decision was cheap to reverse and yours was not.
- Make disagree-and-commit concrete. Name the artefact you left behind so the prediction could be settled without you: the alert and its threshold, the counter on the dashboard, the decision note that recorded the trade-off and the condition that would revisit it.
Follow-up
- What threshold on that alert would have proved you right, and did anyone ever look at it?
- If the same proposal arrived tomorrow with the same deadline, would you argue it the same way?
Tell callers you do not own that their integration breaks
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
- 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.
- 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.
- 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.
- 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?
- 01
A colleague's change updates a row with UPDATE resource SET version = version + 1 WHERE resource_id = $1 AND version = $2 and treats an affected-row count of zero as a successful no-op. You read that as a silently lost update; they think returning 200 is friendlier to clients than returning a conflict. Describe how you have handled a review disagreement of this shape: what goes in the comment, when you leave the thread, and who decides. Then write the comment you would leave here, in under 80 words.
- 02
Describe a design you argued against and lost. State the failure you predicted as a named mechanism, not a feeling about complexity: two services that would need one transaction, a projection with no rebuild path, a write path with no idempotency key. Say what evidence you brought, what the decision maker weighed instead, and what you did after the decision was made: what you instrumented, what you wrote down, and whether the prediction came true. Five minutes.
- 03
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.
What is the difficulty level of the coding rounds?
Candidates generally describe the coding rounds as average to easy. The focus is more on practical coding, clean implementation, and understanding fundamental data structures rather than solving highly complex, abstract algorithmic puzzles.
Metadata Software Engineer candidate reports ↗How long does the entire interview process take?
The process is known to be very efficient. Successful loops can be completed in as little as one week, though this can vary depending on candidate availability and the specific team.
Metadata Software Engineer candidate reports ↗Is there a take-home assignment?
Yes, some candidates receive a take-home assessment task early in the process, which is typically followed by a technical review interview to discuss the implementation details.
Metadata Software Engineer candidate reports ↗What technologies are most important to prepare for?
You should focus heavily on Java, Spring Boot, Hibernate (specifically understanding performance issues like the N+1 problem), and relational databases (MySQL).
Metadata Software Engineer candidate reports ↗What topics does Metadata test in interviews?
Metadata interviews most often cover Quality Assurance (QA) Engineering, UX/UI Design, Java, Marketing Analytics, and Customer Success (CSM) Fundamentals. The exact emphasis depends on the specific role you apply for.
Metadata Software Engineer candidate reports ↗Sources & methodology 3 sources ↗
No official company page is cited. Rounds and questions come from candidate reports and PracHub editorial material; each source shows the date it was read.
- 01Metadata Software Engineer candidate reports ↗
Company-reported rounds, questions and FAQ.
Candidate reports · Accessed 2026-09-22 - 02PracHub Software Engineer practice ↗
PracHub practice material, not company-reported.
PracHub page · Accessed 2026-09-22 - 03PracHub preparation framework ↗
PracHub preparation guidance.
PracHub page · Accessed 2026-09-22
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